Department of Electrical Engineering and Computer Science

Electrical engineers and computer scientists are everywhere—in industry and research areas as diverse as computer and communication networks, electronic circuits and systems, lasers and photonics, semiconductor and solid-state devices, nanoelectronics, biomedical engineering, computational biology, artificial intelligence, robotics, design and manufacturing, control and optimization, computer algorithms, games and graphics, software engineering, computer architecture, cryptography and computer security, power and energy systems, financial analysis, and many more. The infrastructure and fabric of the information age, including technologies such as the internet and the web, search engines, cell phones, high-definition television, and magnetic resonance imaging, are largely the result of innovations in electrical engineering and computer science. The Department of Electrical Engineering and Computer Science (EECS) at MIT and its graduates have been at the forefront of a great many of these advances. Current work in the department holds promise of continuing this record of innovation and leadership, in both research and education, across the full spectrum of departmental activity.

The career paths and opportunities for EECS graduates cover a wide range and continue to grow: fundamental technologies, devices, and systems based on electrical engineering and computer science are pervasive and essential to improving the lives of people around the world and managing the environments they live in. The basis for the success of EECS graduates is a deep education in engineering principles, built on mathematical, computational, physical, and life sciences, and exercised with practical applications and project experiences in a breadth of areas. Our graduates have also demonstrated over the years that EECS provides a strong foundation for those whose work and careers develop in areas quite removed from their origins in engineering.

Undergraduate students in the department take two core subjects that introduce electrical engineering and computer science, and then systematically build up broad foundations and depth in selected intellectual theme areas that match their individual interests. Laboratory subjects, independent projects, and research provide engagement with principles and techniques of analysis, design, and experimentation in a variety of fields. The department also offers a range of programs that enable students to gain experience in industrial settings, ranging from collaborative industrial projects done on campus to term-long experiences at partner companies.

Graduate study in the department moves students toward mastery of areas of individual interest, through coursework and significant research, often defined in interdisciplinary areas that take advantage of the tremendous range of faculty expertise in the department and, more broadly, across MIT.

Undergraduate Study

For MIT undergraduates, the Department of Electrical Engineering and Computer Science offers several programs leading to the Bachelor of Science:

  • The 6-1 program leads to the Bachelor of Science in Electrical Science and Engineering. It is accredited by the Engineering Accreditation Commission of ABET.
  • The 6-2 program leads to the Bachelor of Science in Electrical Engineering and Computer Science and is for those whose interests cross this traditional boundary. It is accredited by both the Engineering and Computing Accreditation Commissions of ABET.
  • The 6-3 program leads to the Bachelor of Science in Computer Science and Engineering. It is accredited by both the Engineering and Computing Accreditation Commissions of ABET.
  • The 6-7 program, offered jointly by the Department of Electrical Engineering and Computer Science and the Department of Biology (Course 7), is for students specializing in computer science and molecular biology. A detailed description of the list of requirements for this degree program may be found in the section on Interdisciplinary Programs.

The bachelor's programs in 6-1, 6-2, and 6-3 build on the General Institute Requirements in science and the humanities, and are structured to provide early, hands-on engagement with ideas, activities, and learning that allow students to experience the range and power of electrical engineering and computer science in an integrated way. The required introductory core subjects 6.01 Introduction to EECS via Robot Sensing, Software and Control and 6.02 Introduction to EECS via Communications Networks both involve substantial work in the laboratory. These are complemented by two mathematics subjects and followed by a choice of three or four foundation courses (depending on the program selected) from a set of subjects that provide the basis for subsequent specialization. Students define their specialization by selecting three header subjects, a department laboratory subject, and two advanced undergraduate subjects from a quite extensive set of possibilities, and also carry out an advanced undergraduate project. Combining these with the four free electives permits students considerable latitude in shaping their program to match diverse interests, while ensuring depth and mastery in a few selected areas.

All students in any EECS Bachelor of Science program may also apply for one of the Master of Engineering programs offered by the department, which require an additional year of study for the simultaneous award of both degrees.

Minor in Computer Science

The department offers a minor in Computer Science; the requirements are as follows:

Required Subjects
Select one of the following:12
Introduction to Computer Science and Programming
Introduction to Computer Science Programming in Python
and Introduction to Computational Thinking and Data Science
6.042[J]Mathematics for Computer Science12
6.006Introduction to Algorithms12
6.009Fundamentals of Programming12
Electives
Select two subjects from the following lists, one of which must be from the Advanced Level list:24-27
Basic Level
Computation Structures
Introduction to Inference
Artificial Intelligence
Advanced Level
Elements of Software Construction
Computer System Engineering
Introduction to Machine Learning
Automata, Computability, and Complexity
Design and Analysis of Algorithms
Software Studio
Total Units72-75

A minimum of four subjects (48 units) taken for the Computer Science Minor cannot also count toward a major or another minor.

Inquiries

Additional information about the department’s undergraduate programs may be obtained from the EECS Undergraduate Office, Room 38-476, 617-253-7329.

Graduate Study

Master of Engineering

The Department of Electrical Engineering and Computer Science permits qualified MIT undergraduate students to apply for one of three Master of Engineering (MEng) programs. These programs consist of an additional, fifth year of study beyond one of the Bachelor of Science programs offered by the department.

Recipients of a Master of Engineering degree normally receive a Bachelor of Science degree simultaneously. No thesis is explicitly required for the Bachelor of Science degree. However, every program must include a major project experience at an advanced level, culminating in written and oral reports.

The Master of Engineering degree also requires completion of 24 units of thesis credit under 6.THM Master of Engineering Program Thesis. While a student may register for more than this number of thesis units, only 24 units count toward the degree requirement. Adjustments to the department requirements are made on an individual basis when it is clear that a student would be better served by a variation in the requirements because of a student’s strong prior background.

Programs leading to the five-year Master of Engineering degree or to the four-year Bachelor of Science degrees can easily be arranged to be identical through the junior year. At the end of the junior year, students with strong academic records may apply to continue through the five-year master’s program. Admission to the Master of Engineering program is open only to undergraduate students who have completed their junior year in the Department of Electrical Engineering and Computer Science at MIT. Students with other preparation seeking a master’s level experience in EECS at MIT should see the Master of Science program described later in this section.

A student in the Master of Engineering program must be registered as a graduate student for at least one regular (non-summer) term. To remain in the program and to receive the Master of Engineering degree, students will be expected to maintain strong academic records.

Three MEng Programs are available:

  • The Master of Engineering in Electrical Engineering and Computer Science (6-P) program is intended to provide the depth of knowledge and the skills needed for advanced graduate study and for professional work, as well as the breadth and perspective essential for engineering leadership in an increasingly complex technological world.
  • The 6-A Master of Engineering Thesis Program with Industry combines the Master of Engineering academic program with periods of industrial practice at affiliated companies. An undergraduate wishing to pursue this degree should initially register for one of the department’s three bachelor’s programs.
  • The Department of Electrical Engineering and Computer Science jointly offers a Master of Engineering in Computer Science and Molecular Biology (6-7P) with the Department of Biology (Course 7). This program is modeled on 6-P program, but provides additional depth in computational biology through coursework and a substantial thesis.

Master of Engineering in Electrical Engineering and Computer Science (Course 6-P)

Through a seamless, five-year course of study, the Master of Engineering in Electrical Engineering and Computer Science (6-P) program leads directly to the simultaneous awarding of the Master of Engineering and one of the three bachelor’s degrees offered by the department. The 6-P program is intended to provide the skills and depth of knowledge in a selected field of concentration needed for advanced graduate study and for professional work, as well as the breadth and perspective essential for engineering leadership in an increasingly complex technological world. The student selects 42 units from a list of subjects approved by the Graduate Office; these subjects, considered along with the two advanced undergraduate subjects from the bachelor’s program, must include at least 36 units in an area of concentration. A further 24 units of electives are chosen from a restricted departmental list of mathematics, science, and engineering subjects.

Master of Engineering Thesis Program with Industry (Course 6-A)

The 6-A Master of Engineering Thesis Program with Industry enables students to combine classroom studies with practical experience in industry through a series of supervised work assignments at one of the companies or laboratories participating in the program, culminating with a Master of Engineering thesis performed at a 6-A member company. Collectively, the participating companies provide a wide spectrum of assignments in the various fields of electrical engineering and computer science, as well as an exposure to the kinds of activities in which engineers are currently engaged. Since a continuing liaison between the companies and faculty of the department is maintained, students receive assignments of progressive responsibility and sophistication that are usually more professionally rewarding than typical summer jobs.

The 6-A program is primarily designed to work in conjunction with the department's five-year Master of Engineering degree program. Internship students generally complete three assignments with their cooperating company—usually two summers and one regular term. While on 6-A assignment, students receive pay from the participating company as well as academic credit for their work. During their graduate year, 6-A students generally receive a 6-A fellowship or a research or teaching assistantship to help pay for the graduate year.

The department conducts a fall recruitment during which juniors who wish to work toward an industry-based Master of Engineering thesis may apply for admission to the 6-A program. Acceptance of a student into the program cannot be guaranteed, as openings are limited. At the end of their junior year, most 6-A students can apply for admission to 6-PA, which is the 6-A version of the department's five-year 6-P Master of Engineering degree program. 6-PA students do their Master of Engineering thesis at their participating company's facilities. They can apply up to 24 units of work-assignment credit toward their Master of Engineering degree. The first 6-A assignment may be used for the advanced undergraduate project that is required for award of a bachelor's degree, by including a written report and obtaining approval by a faculty member.

At the conclusion of their program, 6-A students are not obliged to accept employment with the company, nor is the company obliged to offer such employment.

Additional information about the program is available at the 6-A Office, Room 38-409E, 617-253-4644.

Master of Engineering in Computer Science and Molecular Biology (Course 6-7P)

The Department of Electrical Engineering and Computer Science jointly offers a Master of Engineering in Computer Science and Molecular Biology (6-7P) with the Department of Biology (Course 7). A detailed description of the list of requirements for this degree program may be found under the section on Interdisciplinary Programs.

Predoctoral and Doctoral Programs

The programs of education offered by the Department of Electrical Engineering and Computer Science at the doctoral and predoctoral level have three aspects. First, a variety of classroom subjects in physics, mathematics, and fundamental fields of electrical engineering and computer science is provided to permit students to develop strong scientific backgrounds. Second, more specialized classroom and laboratory subjects and a wide variety of colloquia and seminars introduce the student to the problems of current interest in many fields of research, and to the techniques that may be useful in attacking them. Third, each student conducts research under the direct supervision of a member of the faculty and reports the results in a thesis.

Three advanced degree programs are offered in addition to the Master of Engineering program described above. A well-prepared student with a bachelor's degree in an appropriate field from some school other than MIT (or from another department at MIT) normally requires about one and one-half to two years to complete the formal studies and the required thesis research in the Master of Science degree program. (Students who have been undergraduates in Electrical Engineering and Computer Science at MIT and who seek opportunities for further study must complete the Master of Engineering rather than the Master of Science degree program.) With an additional year of study and research beyond the master's level, a student in the doctoral or predoctoral program can complete the requirements for the degree of Electrical Engineer or Engineer in Computer Science. The doctoral program usually takes about four to five years beyond the master's level.

There are no fixed programs of study for these doctoral and predoctoral degrees. Each student plans a program in consultation with a faculty advisor. As the program moves toward thesis research, it usually centers in one of a number of areas, each characterized by an active research program. Areas of specialization in the department that have active research programs and related graduate subjects include communications, control, signal processing, and optimization; computer science; artificial intelligence, robotics, computer vision, and graphics; electronics, computers, systems, and networks; electromagnetics and electrodynamics; optics, photonics, and quantum electronics; energy conversion devices and systems; power engineering and power electronics; materials and devices; VLSI system design and technology; nanoelectronics; bioelectrical engineering; and computational biology.

In addition to graduate subjects in electrical engineering and computer science, many students find it profitable to study subjects in other departments such as Biology, Economics, Linguistics and Philosophy, Management, Mathematics, Physics, and Brain and Cognitive Sciences.

The informal seminar is an important mechanism for bringing together members of the various research groups. Numerous seminars meet every week. In these, graduate students, faculty, and visitors report their research in an atmosphere of free discussion and criticism. These open seminars are excellent places to learn about the various research activities in the department.

Research activities in electrical engineering and computer science are carried on by students and faculty in laboratories of extraordinary range and strength, including the Laboratory for Information and Decision Systems, Research Laboratory of Electronics, Computer Science and Artificial Intelligence Laboratory, Center for Materials Science and Engineering, Laboratory for Energy and the Environment (see MIT Energy Initiative), Kavli Institute for Astrophysics and Space Research, Lincoln Laboratory, Media Laboratory, Francis Bitter Magnet Laboratory, Operations Research Center, Plasma Science and Fusion Center, and the Microsystems Technology Laboratories. Descriptions of many of these laboratories may be found under the section on Research and Study.

Because the backgrounds of applicants to the department's doctoral and predoctoral programs are extremely varied, both as to field (electrical engineering, computer science, physics, mathematics, biomedical engineering, etc.) and as to level of previous degree (bachelor's or master's), no specific admissions requirements are listed. All applicants for any of these advanced programs will be evaluated in terms of their potential for successful completion of the department's doctoral program. Superior achievement in relevant technical fields is considered particularly important.

Master of Science in Electrical Engineering and Computer Science

The general requirements for the degree of Master of Science are listed under Graduate Education. The department requires that the 66-unit program consist of at least four subjects from a list of approved subjects by the Graduate Office which must include a minimum of 42 units of advanced graduate subjects. In addition, a 24-unit thesis is required beyond the 66 units. Students working full-time for the Master of Science degree may take as many as four classroom subjects per term. The subjects are wholly elective and are not restricted to those given by the department. The program of study must be well balanced, emphasizing one or more of the theoretical or experimental aspects of electrical engineering or computer science.

Electrical Engineer or Engineer in Computer Science

The general requirements for an engineer's degree are given under the section on Graduate Education. These degrees are open to those able students in the doctoral or predoctoral program who seek more extensive training and research experiences than are possible within the master's program. Admission to the engineer's program depends upon a superior academic record and outstanding progress on a thesis. The course of studies consists of at least 162 units, 90 of which must be from a list of subjects approved by the Graduate Office, and the thesis requirements for a master's degree.

Doctor of Philosophy or Doctor of Science

The general requirements for the degree of Doctor of Philosophy or Doctor of Science are given under the section on Graduate Education. Doctoral candidates are expected to participate fully in the educational program of the department and to perform thesis work that is a significant contribution to knowledge. As preparation, MIT students in the Master of Engineering in Electrical Engineering and Computer Science program will be expected to complete that program. Students who have received a bachelor's degree outside the department, but who have not completed a master's degree program, will normally be expected to complete the requirements for the Master of Science degree described earlier, including a thesis. Students who have completed a master's degree elsewhere without a significant research component will be required to register for and carry out a research accomplishment equivalent to a master's thesis before being allowed to proceed in the doctoral program.

Details of how students in the department fulfill the requirements for the doctoral program are spelled out in an internal memorandum. The department does not have a foreign language requirement, but does require an approved minor program.

Graduate students enrolled in the department may participate in the research centers described in the Research and Study section, such as the Center for Biomedical Engineering and the Operations Research Center.

Financial Support

Master of Engineering Degree Students

Students in the fifth year of study toward the Master of Engineering degree are commonly supported by a graduate teaching or research assistantship. In the 6-A Master of Engineering Thesis Program with Industry students are supported by paid company internships. Students supported by full-time research or teaching assistantships may register for no more than two regular classes totaling at most 27 units. They receive additional academic units for their participation in the teaching or research program. Support through an assistantship may extend the period required to complete the Master of Engineering program by an additional term or two. Support is granted competitively to graduate students and may not be available for all of those admitted to the Master of Engineering program. The MEng degree is normally completed by students taking a full load of regular subjects in two graduate terms. Students receiving assistantships commonly require a third term and may petition to continue for a fourth graduate term.

Master of Science, Engineer, and Doctoral Degree Students

Studies toward an advanced degree can be supported by personal funds, by an award such as the National Science Foundation Fellowship (which the student brings to MIT), by a fellowship or traineeship awarded by MIT, or by a graduate assistantship. Assistantships require participation in research or teaching in the department or in one of the associated laboratories. Full-time assistants may register for no more than two scheduled classroom or laboratory subjects during the term, but may receive additional academic credit for their participation in the teaching or research program.

Inquiries

Additional information concerning graduate academic and research programs, admissions, financial aid, and assistantships may be obtained from the Electrical Engineering and Computer Science Graduate Office, Room 38-444, 617-253-4605, or visit the website.

Interdisciplinary Programs

Computation for Design and Optimization

The Computation for Design and Optimization (CDO) program offers a master's degree to students interested in the analysis and application of computational approaches to designing and operating engineered systems. The curriculum is designed with a common core serving all engineering disciplines and an elective component focusing on specific applications. Current MIT graduate students may pursue a CDO master's degree in conjunction with a department-based master's or PhD program. For more information, see the full program description under Interdisciplinary Graduate Programs.

Joint Program with the Woods Hole Oceanographic Institution

The Joint Program with the Woods Hole Oceanographic Institution (WHOI) is intended for students whose primary career objective is oceanography or oceanographic engineering. Students divide their academic and research efforts between the campuses of MIT and WHOI. Joint Program students are assigned an MIT faculty member as academic advisor; thesis research may be supervised by MIT or WHOI faculty. While in residence at MIT, students follow a program similar to that of other students in their home department. The program is described in more detail under Interdisciplinary Graduate Programs.

Leaders for Global Operations

The 24-month Leaders for Global Operations (LGO) program combines graduate degrees in engineering and management for those with previous postgraduate work experience and strong undergraduate degrees in a technical field. During the two-year program, students complete a six-month internship at one of LGO's partner companies, where they conduct research that forms the basis of a dual-degree thesis. Students finish the program with two MIT degrees: an MBA (or SM in management) and an SM from one of six engineering programs, some of which have optional or required LGO tracks. After graduation, alumni take on leadership roles at top global manufacturing and operations companies

System Design and Management

The System Design and Management (SDM) program is a partnership among industry, government, and the university for educating technically grounded leaders of 21st-century enterprises. Jointly sponsored by the School of Engineering and the Sloan School of Management, it is MIT's first degree program to be offered with a distance learning option in addition to a full-time in-residence option.

Technology and Policy

The Master of Science in Technology and Policy is an engineering research degree with a strong focus on the role of technology in policy analysis and formulation. The Technology and Policy Program (TPP) curriculum provides a solid grounding in technology and policy by combining advanced subjects in the student's chosen technical field with courses in economics, politics, and law. Many students combine TPP's curriculum with complementary subjects to obtain dual degrees in TPP and either a specialized branch of engineering or an applied social science such as political science or urban studies and planning. For additional information, see the program description under the Institute for Data, Systems, and Society.

Faculty and Teaching Staff

Anantha P. Chandrakasan, PhD

Joseph F. and Nancy P. Keithley Professor in Electrical Engineering

Head, Department of Electrical Engineering and Computer Science

Professors

Harold Abelson, PhD

Class of 1992 Professor

Professor of Computer Science and Engineering

Professor of Media Arts and Sciences

Elfar Adalsteinsson, PhD

Professor of Electrical Engineering and Computer Science

Core Faculty, Institute for Medical Engineering and Science

Anant Agarwal, PhD

Professor of Computer Science and Engineering

Akintunde I. Akinwande, PhD

Professor of Electrical Engineering

Saman P. Amarasinghe, PhD

Professor of Computer Science and Engineering

Dimitri A. Antoniadis, PhD

Ray and Maria Stata Professor of Electrical Engineering

Arthur B. Baggeroer, ScD

Professor of Mechanical and Ocean Engineering

Professor of Electrical Engineering

Hari Balakrishnan, PhD

Fujitsu Professor in Computer Science and Engineering

Marc A. Baldo, PhD

Professor of Electrical Engineering

Regina Barzilay, PhD

Delta Electronics Professor of Computer Science and Engineering

Bonnie Berger, PhD

Professor of Applied Mathematics

Professor of Computer Science

Member, Health Sciences and Technology Faculty

Karl K. Berggren, PhD

Professor of Electrical Engineering and Computer Science

Tim Berners-Lee, BA

3 Com Founders Professor of Engineering

Professor of Electrical Engineering and Computer Science

Dimitri P. Bertsekas, PhD

Professor of Electrical Engineering

Member, Institute for Data, Systems, and Society

Robert C. Berwick, PhD

Professor of Computer Science and Engineering

Member, Institute for Data, Systems, and Society

Sangeeta N. Bhatia, MD, PhD

John and Dorothy Wilson Professor of Biochemistry

Professor of Electrical Engineering

Core Faculty, Institute for Medical Engineering and Science

Duane S. Boning, PhD

Clarence J. LeBel Professor of Electrical Engineering

Louis D. Braida, PhD

Henry Ellis Warren (1894) Professor

Professor of Electrical Engineering

Member, Health Sciences and Technology Faculty

Rodney Brooks, PhD

Professor of Computer Science and Engineering

Vladimir Bulovic, PhD

Fariborz Maseeh (1990) Professor in Emerging Technology

Associate Dean for Innovation,

Professor of Electrical Engineering

Vincent W. S. Chan, PhD

Joan and Irwin M. (1957) Jacobs Professor

Professor of Electrical Engineering

Isaac L. Chuang, PhD

Professor of Electrical Engineering

Professor of Physics

Munther A. Dahleh, PhD

William A. Coolidge Professor

Professor of Electrical Engineering and Computer Science

Director, Institute for Data, Systems, and Society

Luca Daniel, PhD

Professor of Electrical Engineering

Randall Davis, PhD

Professor of Computer Science and Engineering

Jesús A. del Alamo, PhD

Donner Professor of Science

Professor of Electrical Engineering

Erik D. Demaine, PhD

Professor of Computer Science and Engineering

Srinivas Devadas, PhD

Edwin Sibley Webster Professor of Electrical Engineering and Computer Science

Fredo Durand, PhD

Professor of Computer Science and Engineering

Yoel Fink, PhD

Professor of Materials Science and Engineering

Professor of Electrical Engineering and Computer Science

Clifton G. Fonstad Jr, PhD

Professor of Electrical Engineering

Dennis M. Freeman, PhD

Dean for Undergraduate Education

Professor of Electrical Engineering

Member, Health Sciences and Technology Faculty

William T. Freeman, PhD

Thomas and Gerd Perkins Professor

Professor of Computer Science and Engineering

James G. Fujimoto, PhD

Elihu Thomson Professor in Electrical Engineering

Robert G. Gallager, ScD

Professor of Electrical Engineering

David K. Gifford, PhD

Professor of Computer Science and Engineering

Shafi Goldwasser, PhD

RSA Professor

Professor of Computer Science and Engineering

Polina Golland, PhD

Professor of Computer Science and Engineering

Member, Institute for Data, Systems, and Society

Martha L. Gray, PhD

J.W. Kieckhefer Professor of Health Sciences and Technology

Professor of Medical and Electrical Engineering

Core Faculty, Institute for Medical Engineering and Science

W. Eric L. Grimson, PhD

Bernard M. Gordon Professor in Medical Engineering

Professor of Electrical Engineering and Computer Science

Chancellor for Academic Advancement

Alan J. Grodzinsky, ScD

Professor of Biological Engineering

Professor of Electrical Engineering

Professor of Mechanical Engineering

John V. Guttag, PhD

Dugald C. Jackson Professor in Electrical Engineering

Professor of Computer Science and Engineering

Jongyoon Han, PhD

Professor of Electrical Engineering

Professor of Biological Engineering

Frederick C. Hennie III, PhD

Professor of Computer Science and Engineering

Berthold Klaus Paul Horn, PhD

Professor of Computer Science and Engineering

Judy L. Hoyt, PhD

Professor of Electrical Engineering

Qing Hu, PhD

Distinguished Professor

Professor of Electrical Engineering

Piotr Indyk, PhD

Professor of Computer Science and Engineering

Tommi S. Jaakkola, PhD

Professor of Computer Science and Engineering

Member, Institute for Data, Systems, and Society

Daniel Jackson, PhD

Professor of Computer Science and Engineering

Patrick Jaillet, PhD

Dugald C. Jackson Professor in Electrical Engineering

Member, Institute for Data, Systems, and Society

M. Frans Kaashoek, PhD

Charles A. Piper (1935) Professor

Professor of Computer Science and Engineering

Leslie P. Kaelbling, PhD

Professor of Computer Science and Engineering

David R. Karger, PhD

Professor of Computer Science and Engineering

John G. Kassakian, ScD

Professor of Electrical Engineering

Dina Katabi, PhD

Andrew (1956) and Erna Viterbi Professor

Professor of Computer Science and Engineering

Manolis Kellis, PhD

Professor of Electrical Engineering and Computer Science

James L. Kirtley Jr, PhD

Professor of Electrical Engineering

Leslie A. Kolodziejski, PhD

Professor of Electrical Engineering

Jing Kong, PhD

Professor of Electrical Engineering

Jeffrey H. Lang, PhD

Vitesse Professor

Professor of Electrical Engineering

Hae-Seung Lee, PhD

Advanced Television and Signal Processing Professor

Professor of Electrical Engineering

Steven B. Leeb, PhD

Professor of Electrical Engineering

Professor of Mechanical Engineering

Charles E. Leiserson, PhD

Edwin Sibley Webster Professor of Electrical Engineering

Professor of Computer Science and Engineering

Jae S. Lim, PhD

Professor of Electrical Engineering

Barbara H. Liskov, PhD

Institute Professor

Professor of Computer Science

Andrew W. Lo, PhD

Charles E. and Susan T. Harris Professor

Professor of Finance

Professor of Electrical Engineering and Computer Science

Member, Institute for Data, Systems, and Society

Tomás Lozano-Pérez, PhD

Professor of Computer Science and Engineering

Nancy Ann Lynch, PhD

NEC Professor of Software Science and Engineering

Samuel R. Madden, PhD

Professor of Computer Science and Engineering

Thomas L. Magnanti, PhD

Institute Professor

Professor of Operations Research

Professor of Electrical Engineering

Member, Institute for Data, Systems, and Society

Roger Greenwood Mark, MD, PhD

Distinguished Professor in Health Sciences and Technology

Core Faculty, Institute for Medical Engineering and Science

Professor of Electrical Engineering and Computer Science

Muriel Médard, ScD

Cecil H. Green Professor in Electrical Engineering

Alexandre Megretski, PhD

Professor of Electrical Engineering

Member, Institute for Data, Systems, and Society

Albert R. Meyer, PhD

Hitachi America Professor of Engineering

Professor of Computer Science and Engineering

Silvio Micali, PhD

Ford Foundation Professor of Engineering

Professor of Computer Science and Engineering

Robert C. Miller, PhD

Professor of Computer Science and Engineering

Arvind Mithal, PhD

Charles W. and Jennifer C. Johnson Professor in Computer Science and Engineering

Sanjoy K. Mitter, PhD

Professor of Electrical Engineering

Member, Institute for Data, Systems, and Society

Robert T. Morris, PhD

Professor of Computer Science and Engineering

Joel Moses, PhD

Institute Professor

Professor of Electrical Engineering and Computer Science

Member, Institute for Data, Systems, and Society

Alan V. Oppenheim, PhD

Ford Professor of Engineering

Professor of Electrical Engineering and Computer Science

Terry Philip Orlando, PhD

Professor of Electrical Engineering

Asuman E. Ozdaglar, PhD

Professor of Electrical Engineering

Associate Director, Institute for Data, Systems, and Society

Member, Institute for Data, Systems, and Society

Tomás Palacios, PhD

Professor of Electrical Engineering

Pablo A. Parrilo, PhD

Professor of Electrical Engineering and Computer Science

Member, Institute for Data, Systems, and Society

Li-Shiuan Peh, PhD

Professor of Electrical Engineering and Computer Science

David J. Perreault, PhD

Professor in Power Engineering

Rajeev J. Ram, PhD

Professor of Electrical Engineering

L. Rafael Reif, PhD

President

Professor of Electrical Engineering

Martin C. Rinard, PhD

Professor of Computer Science and Engineering

Ronald L. Rivest, PhD

Institute Professor

Professor of Computer Science and Engineering

Ronitt Rubinfeld, PhD

Professor of Computer Science and Engineering

Daniela L. Rus, PhD

Andrew (1956) and Erna Viterbi Professor

Professor of Computer Science and Engineering

Martin A. Schmidt, PhD

Ray and Maria Stata Professor of Electrical Engineering and Computer Science

Provost

Devavrat Shah, PhD

Professor of Electrical Engineering and Computer Science

Member, Institute for Data, Systems, and Society

Jeffrey H. Shapiro, PhD

Professor of Electrical Engineering

Nir N. Shavit

Professor of Computer Science and Engineering

Charles G. Sodini, PhD

Clarence J. Lebel Professor in Electrical Engineering

Collin M. Stultz, MD, PhD

Professor of Electrical Engineering and Computer Science

Core Faculty, Institute for Medical Engineering and Science

Gerald Jay Sussman, PhD

Panasonic Professor

Professor of Electrical Engineering

Peter Szolovits, PhD

Professor of Computer Science and Engineering

Member, Health Sciences and Technology Faculty

Russell L. Tedrake, PhD

Professor of Computer Science and Engineering

Professor of Aeronautics and Astronautics

Professor of Mechanical Engineering

Bruce Tidor, PhD

Professor of Electrical Engineering and Computer Science

Professor of Biological Engineering

Antonio Torralba, PhD

Professor of Electrical Engineering and Computer Science

John N. Tsitsiklis, PhD

Clarence J. Lebel Professor in Electrical Engineering

Member, Institute for Data, Systems, and Society

George C. Verghese, PhD

Henry Ellis Warren Professor of Electrical Engineeering

Joel Voldman, PhD

Professor of Electrical Engineering

Stephen A. Ward, PhD

Professor of Computer Science and Engineering

Cardinal Warde, PhD

Professor of Electrical Engineering

Ron Weiss, PhD

Professor of Computer Science

Professor of Biological Engineering

Jacob K. White, PhD

Cecil H. Green Professor in Electrical Engineering

Alan S. Willsky, PhD

Professor of Electrical Engineering

Member, Institute for Data, Systems, and Society

Patrick Henry Winston, PhD

Ford Foundation Professor of Engineering

Professor of Electrical Engineering and Computer Science

Gregory W. Wornell, PhD

Sumitomo Electric Industries Professor in Engineering

Professor of Electrical Engineering

Member, Institute for Data, Systems, and Society

Lizhong Zheng, PhD

Professor of Electrical Engineering

Victor W. Zue, ScD

Delta Electronics Professor

Professor of Electrical Engineering and Computer Science

Associate Professors

Adam Chlipala, PhD

Associate Professor of Electrical Engineering and Computer Science

Konstantinos Daskalakis, PhD

Associate Professor of Computer Science and Engineering

Dirk Robert Englund, PhD

Associate Professor of Electrical Engineering and Computer Science

Peter L. Hagelstein, PhD

Associate Professor of Electrical Engineering

Timothy K. Lu, MD, PhD

Associate Professor of Electrical Engineering and Computer Science

Associate Professor of Biological Engineering

Wojciech Matusik, PhD

Associate Professor of Electrical Engineering and Computer Science

Yury Polyanskiy, PhD

Associate Professor of Electrical Engineering and Computer Science

Member, Institute for Data, Systems, and Society

Armando Solar Lezama, PhD

Associate Professor of Computer Science and Engineering

Vinod Vaikuntanathan, PhD

Steven G. (1968) and Renée Finn Career Development Professor

Associate Professor of Computer Science and Engineering

Michael Watts, PhD

Associate Professor of Electrical Engineering and Computer Science

Nickolai Zeldovich, PhD

Associate Professor of Computer Science and Engineering

Assistant Professors

Mohammadreza Alizadeh Attar, PhD

Assistant Professor of Electrical Engineering and Computer Science

Guy Bresler, PhD

Bonnie and Marty (1964) Tennenbaum Career Development Professor

Assistant Professor of Electrical Engineering and Computer Science

Member, Institute for Data, Systems, and Society

Tamara Broderick, PhD

ITT Career Development Professor in Computer Technology

Member, Institute for Data, Systems, and Society

Michael J. Carbin, PhD

Jamieson Career Development Professor

Assistant Professor of Electrical Engineering and Computer Science

Ruonan Han, PhD

E. E. Landsman (1958) Career Development Professor

Assistant Professor of Electrical Engineering and Computer Science

Thomas Heldt, PhD

W. M. Keck Career Development Professor

Assistant Professor of Electrical Engineering and Computer Science

Core Faculty, Institute for Medical Engineering and Science

Stefanie Jegelka, ScD

X-Window Consortium Career Development Professor

Assistant Professor of Computer Science and Engineering

Member, Institute for Data, Systems, and Society

Luqiao Liu, PhD

Robert J. Shillman Career Development Professor

Assistant Professor of Electrical Engineering and Computer Science

Aleksander Madry, PhD

Assistant Professor of Computer Science and Engineering

Daniel Sánchez, PhD

Assistant Professor of Electrical Engineering and Computer Science

Max M. Shulaker, PhD

Assistant Professor of Electrical Engineering and Computer Science

Justin Solomon, PhD

Assistant Professor of Electrical Engineering and Computer Science

Vivienne Sze, PhD

Emanuel E. Landsman (1958) Career Development Professor

Assistant Professor of Electrical Engineering and Computer Science

Caroline Uhler, PhD

Henry L. and Grace Doherty Professor in Ocean Utilization

Assistant Professor of Electrical Engineering and Computer Science

Member, Institute for Data, Systems, and Society

Matei A. Zaharia, PhD

Assistant Professor of Computer Science and Engineering

Professors of the Practice

Joel S. Emer, PhD

Professor of the Practice of Electrical Engineering and Computer Science

Joel E. Schindall, PhD

Bernard M. Gordon Professor in Product Engineering

Professor of the Practice of Electrical Engineering and Computer Science

Adjunct Professors

G. David Forney, ScD

Adjunct Professor of Electrical Engineering

Adjunct Professor of Data, Systems, and Society

Butler W. Lampson, PhD

Adjunct Professor of Computer Science and Engineering

Michael Stonebraker, PhD

Adjunct Professor of Computer Science and Engineering

Senior Lecturers

Tony Eng, PhD

Senior Lecturer in Electrical Engineering and Computer Science

Christopher J. Terman, PhD

Senior Lecturer of Electrical Engineering and Computer Science

Lecturers

Ana Bell, PhD

Lecturer in Electrical Engineering and Computer Science

Max Goldman

Lecturer in Electrical Engineering and Computer Science

Silvina Z. Hanono Wachman, PhD

Lecturer in Electrical Engineering and Computer Science

Adam J. Hartz, MEng

Lecturer in Electrical Engineering and Computer Science

Gim P. Hom, MS, MS, EE

Lecturer in Electrical Engineering and Computer Science

Katrina Leigh LaCurts, PhD

Lecturer in Electrical Engineering and Computer Science

Joseph Daly Steinmeyer, PhD

Lecturer in Electrical Engineering and Computer Science

Technical Instructors

Gavin Darcey, MEng

Technical Instructor of Electrical Engineering and Computer Science

David L. Lewis

Technical Instructor of Electrical Engineering and Computer Science

Scott Poesse, AA

Technical Instructor of Electrical Engineering and Computer Science

Research Staff

Senior Research Scientists

David D. Clark, PhD

Senior Research Scientist of Electrical Engineering and Computer Science

Professors Emeriti

Michael Athans, PhD

Professor Emeritus of Electrical Engineering

James Donald Bruce, ScD

Professor Emeritus of Electrical Engineering

Fernando José Corbató, PhD

Professor Emeritus of Computer Science and Engineering

Jack B. Dennis, ScD

Professor Emeritus of Computer Science and Engineering

Mildred Dresselhaus, PhD

Institute Professor

Professor of Electrical Engineering

Professor of Physics

Murray Eden, PhD

Professor Emeritus of Electrical Engineering

David Jacob Epstein, ScD

Professor Emeritus of Electrical Engineering

Lawrence S. Frishkopf, PhD

Professor Emeritus of Electrical and Bioengineering

Harry Constantine Gatos, PhD

Professor Emeritus of Molecular Engineering

Professor Emeritus of Electronic Materials

Paul E. Gray, ScD

President Emeritus

Professor Emeritus of Electrical Engineering

Carl Eddie Hewitt, PhD

Associate Professor Emeritus of Computer Science and Engineering

Erich P. Ippen, PhD

Elihu Thomson Professor Emeritus

Professor Emeritus of Physics

Professor Emeritus of Electrical Engineering

Robert S. Kennedy, ScD

Professor Emeritus of Electrical Engineering

Francis Fan Lee, PhD

Professor Emeritus of Electrical Engineering and Computer Science

Alan L. McWhorter, ScD

Professor Emeritus of Electrical Engineering

Walter E. Morrow Jr, MS

Professor Emeritus of Electrical Engineering

Ronald R. Parker, PhD

Professor Emeritus of Nuclear Science and Engineering

Professor Emeritus of Electrical Engineering

William T. Peake, ScD

Professor Emeritus of Electrical and Bioengineering

Professor Emeritus of Health Sciences and Technology

Paul L. Penfield Jr, PhD

Professor Emeritus of Electrical Engineering

George W. Pratt Jr, PhD

Professor Emeritus of Electrical Engineering

Jerome H. Saltzer, ScD

Professor Emeritus of Computer Science and Engineering

Herbert Harold Sawin, PhD

Professor Emeritus of Chemical Engineering

Professor Emeritus of Electrical Engineering

Campbell L. Searle, MEng

Professor Emeritus of Electrical Engineering

Stephen D. Senturia, PhD

Professor Emeritus of Electrical Engineering

Henry Ignatius Smith, PhD

Professor Emeritus of Electrical Engineering and Computer Science

Richard D. Thornton, ScD

Professor Emeritus of Electrical Engineering

Thomas F. Weiss, PhD

Professor Emeritus of Electrical and Bioengineering

Professor Emeritus of Health Sciences and Technology

Gerald L. Wilson, PhD

Vannevar Bush Professor Emeritus

Professor Emeritus of Electrical Engineering

Professor Emeritus of Mechanical Engineering

Markus Zahn, ScD

Professor Emeritus of Electrical Engineering

Basic Undergraduate Subjects

6.00 Introduction to Computer Science and Programming

Prereq: None
U (Fall, Spring)
3-7-2 units. REST

Introduction to computer science and programming for students with little or no programming experience. Students learn how to program and how to use computational techniques to solve problems. Topics include software design, algorithms, data analysis, and simulation techniques. Assignments are done using the Python programming language. Meets with 6.0001 first half of term and 6.0002 second half of term. Credit cannot also be received for 6.0001 or 6.0002. Final given during final exam week.

J. V. Guttag

6.0001 Introduction to Computer Science Programming in Python

Prereq: None
U (Fall, Spring; first half of term)
2-3-1 units

Introduction to computer science and programming for students with little or no programming experience. Students develop skills to program and use computational techniques to solve problems. Topics include the notion of computation, Python, simple algorithms and data structures, testing and debugging, and algorithmic complexity. Combination of 6.0001 and 6.0002 counts as REST subject. Final given in the seventh week of the term.

J. V. Guttag

6.0002 Introduction to Computational Thinking and Data Science

Prereq: 6.0001 or permission of instructor
U (Fall, Spring; second half of term)
2-3-1 units

Provides an introduction to using computation to understand real-world phenomena. Topics include plotting, stochastic programs, probability and statistics, random walks, Monte Carlo simulations, modeling data, optimization problems, and clustering. Combination of 6.0001 and 6.0002 counts as REST subject. Final given during final exam week.

J. V. Guttag

6.002 Circuits and Electronics

Prereq: Physics II (GIR); Coreq: 18.03 or 2.087
U (Fall, Spring)
4-1-7 units. REST

Fundamentals of the lumped circuit abstraction. Resistive elements and networks, independent and dependent sources, switches and MOS devices, digital abstraction, amplifiers, and energy storage elements. Dynamics of first- and second-order networks; design in the time and frequency domains; analog and digital circuits and applications. Design exercises. Occasional laboratory.

A. Agarwal, J. del Alamo, J. H. Lang, D. J. Perreault

6.003 Signals and Systems

Prereq: Physics II (GIR); 2.087 or 18.03
U (Fall, Spring)
5-0-7 units. REST

Presents the fundamentals of signal and system analysis. Topics include discrete-time and continuous-time signals, Fourier series and transforms, Laplace and Z transforms, and analysis of linear, time-invariant systems. Applications drawn broadly from engineering and physics, including audio and image processing, communications, and automatic control.

D. M. Freeman, Q. Hu, J. S. Lim

6.004 Computation Structures

Prereq: Physics II (GIR)
U (Fall, Spring)
4-0-8 units. REST

Introduces architecture of digital systems, emphasizing structural principles common to a wide range of technologies. Multilevel implementation strategies; definition of new primitives (e.g., gates, instructions, procedures, and processes) and their mechanization using lower-level elements. Analysis of potential concurrency; precedence constraints and performance measures; pipelined and multidimensional systems. Instruction set design issues; architectural support for contemporary software structures.

S. A. Ward, C. J. Terman

6.005 Elements of Software Construction

Prereq: 6.01; Coreq: 6.042[J]
U (Fall)
4-0-8 units. REST

Introduces fundamental principles and techniques of software development, i.e., how to write software that is safe from bugs, easy to understand, and ready for change. Topics include specifications and invariants; testing, test-case generation, and coverage; abstract data types and representation independence; design patterns for object-oriented programming; concurrent programming, including message passing and shared concurrency, and defending against races and deadlock; and functional programming with immutable data and higher-order functions. Includes weekly programming exercises and larger group programming projects. 12 Engineering Design Points.

D. N. Jackson, R. C. Miller

6.006 Introduction to Algorithms

Prereq: 6.042[J]; 6.01 or Coreq: 6.009
U (Fall, Spring)
4-0-8 units

Introduction to mathematical modeling of computational problems, as well as common algorithms, algorithmic paradigms, and data structures used to solve these problems. Emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.

R. L. Rivest, S. Devadas

6.007 Electromagnetic Energy: From Motors to Solar Cells

Prereq: Physics II (GIR); Coreq: 2.087 or 18.03
U (Fall)
5-1-6 units. REST

Discusses applications of electromagnetic and equivalent quantum mechanical principles to classical and modern devices. Covers energy conversion and power flow in both macroscopic and quantum-scale electrical and electromechanical systems, including electric motors and generators, electric circuit elements, quantum tunneling structures and instruments. Studies photons as waves and particles and their interaction with matter in optoelectronic devices, including solar cells and displays.

V. Bulovic, R. J. Ram

6.008 Introduction to Inference

Prereq: Calculus II (GIR) or permission of instructor
U (Fall)
4-4-4 units. Institute LAB

Introduces probabilistic modeling for problems of inference and machine learning from data, emphasizing analytical and computational aspects. Distributions, marginalization, conditioning, and structure; graphical representations. Belief propagation, decision-making, classification, estimation, and prediction. Sampling methods and analysis. Introduces asymptotic analysis and information measures. Substantial computational laboratory component explores the concepts introduced in class in the context of realistic contemporary applications. Students design inference algorithms, investigate their behavior on real data, and discuss experimental results.

P. Golland, G. W. Wornell

6.009 Fundamentals of Programming (6.S04)

Prereq: 6.0001
U (Fall, Spring)
2-4-6 units. Institute LAB

Introduces fundamental concepts of programming. Designed to develop skills in applying basic methods from programming languages to abstract problems. Topics include programming and Python basics, computational concepts, software engineering, algorithmic techniques, data types, and recursion and tail recursion. Lab component consists of software design, construction, and implementation of design.

A. Chlipala, S. Devadas

6.01 Introduction to EECS via Robot Sensing, Software and Control

Prereq: 6.0001 or permission of instructor
U (Fall, Spring)
2-4-6 units. Institute LAB

An integrated introduction to electrical engineering and computer science, taught using substantial laboratory experiments with mobile robots. Key issues in the design of engineered artifacts operating in the natural world: measuring and modeling system behaviors; assessing errors in sensors and effectors; specifying tasks; designing solutions based on analytical and computational models; planning, executing, and evaluating experimental tests of performance; refining models and designs. Issues addressed in the context of computer programs, control systems, probabilistic inference problems, circuits and transducers, which all play important roles in achieving robust operation of a large variety of engineered systems.

D. M. Freeman, A. Hartz, L. P. Kaelbling, T. Lozano-Perez

6.011 Signals, Systems and Inference

Prereq: 6.003; 6.008, 6.041A, or 18.600
U (Spring)
4-0-8 units

Covers signals, systems and inference in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; and group delay. State feedback and observers. Probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization. Least-mean square error estimation; Wiener filtering. Hypothesis testing; detection; matched filters.

A. V. Oppenheim, G. C. Verghese

6.012 Microelectronic Devices and Circuits

Prereq: 6.002
U (Fall, Spring)
4-0-8 units

Microelectronic device modeling, and basic microelectronic circuit analysis and design. Physical electronics of semiconductor junction and MOS devices. Relating terminal behavior to internal physical processes, developing circuit models, and understanding the uses and limitations of different models. Use of incremental and large-signal techniques to analyze and design transistor circuits, with examples chosen from digital circuits, linear amplifiers, and other integrated circuits. Design project.

A. I. Akinwande, D. A. Antoniadis, J. Kong, C. G. Sodini

6.013 Electromagnetics and Applications

Prereq: Calculus II (GIR), Physics II (GIR)
U (Spring)
3-3-6 units

Analysis and design of modern applications that employ electromagnetic phenomena for signals and power transmission in RF, microwaves, optical and wireless communication systems. Fundamentals include dynamic solutions for Maxwell's equations; electromagnetic power and energy, waves in media, guided waves, radiation, and diffraction; coupling to media and structures; resonance & filters; acoustic analogs. Labs include student hands-on activities from building to testing of devices and systems (e.g. radar) that reinforce lectures, with a focus on fostering creativity and debugging skills. 6.002 and 6.007 are recommended but not required.

L. Daniel, M. R. Watts

6.02 Introduction to EECS via Communications Networks

Prereq: 6.0001
U (Fall)
4-4-4 units. Institute LAB

Studies key concepts, systems, and algorithms to reliably communicate data in settings ranging from the cellular phone network and the Internet to deep space. Weekly laboratory experiments explore these areas in depth. Topics presented in three modules - bits, signals, and packets - spanning the multiple layers of a communication system. Bits module includes information, entropy, data compression algorithms, and error correction with block and convolutional codes. Signals module includes modeling physical channels and noise, signal design, filtering and detection, modulation, and frequency-division multiplexing. Packets module includes switching and queuing principles, media access control, routing protocols, and data transport protocols.

H. Balakrishnan, K. LaCurts, G. C. Verghese,

6.021[J] Cellular Neurophysiology

Same subject as 2.791[J], 20.370[J]
Subject meets with 2.794[J], 6.521[J], 20.470[J], HST.541[J]

Prereq: Physics II (GIR); 18.03; 2.005, 6.002, 6.003, 6.071, 10.301, 20.110[J], or permission of instructor
U (Fall)
5-2-5 units

Integrated overview of the biophysics of cells from prokaryotes to neurons, with a focus on mass transport and electrical signal generation across cell membrane. First half of course focuses on mass transport through membranes: diffusion, osmosis, chemically mediated, and active transport. Second half focuses on electrical properties of cells: ion transport to action potentials in electrically excitable cells. Synaptic transmission. Electrical properties interpreted via kinetic and molecular properties of single voltage-gated ion channels. Laboratory and computer exercises illustrate the concepts. Students taking graduate version complete different assignments. Preference to juniors and seniors.

J. Han, T. Heldt, J. Voldman

6.022[J] Quantitative Systems Physiology

Same subject as 2.792[J], HST.542[J]
Subject meets with 2.796[J], 6.522[J]

Prereq: Physics II (GIR), 18.03, or permission of instructor
U (Spring)
4-2-6 units

Application of the principles of energy and mass flow to major human organ systems. Mechanisms of regulation and homeostasis. Anatomical, physiological and pathophysiological features of the cardiovascular, respiratory and renal systems. Systems, features and devices that are most illuminated by the methods of physical sciences. Laboratory work includes some animal studies. Students taking graduate version complete additional assignments. 2 Engineering Design Points.

T. Heldt, R. G. Mark, C. M. Stultz

6.023[J] Fields, Forces and Flows in Biological Systems

Same subject as 2.793[J], 20.330[J]
Prereq: Physics II (GIR); 2.005, 6.021[J], or permission of instructor, Coreq: 20.309[J]
U (Spring)
4-0-8 units

See description under subject 20.330[J].

J. Han, S. Manalis

6.024[J] Molecular, Cellular, and Tissue Biomechanics

Same subject as 2.797[J], 3.053[J], 20.310[J]
Prereq: 2.370 or 2.772[J]; 18.03 or 3.016; Biology (GIR)
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: U (Spring)

4-0-8 units

See description under subject 20.310[J].

R. D. Kamm, A. J. Grodzinsky, K. Van Vliet

6.025[J] Medical Device Design

Same subject as 2.750[J]
Subject meets with 2.75[J], 6.525[J], HST.552[J]

Prereq: 2.70, 2.72, 2.678, 6.115, 22.071, or permission of instructor
U (Fall)
3-0-9 units

See description under subject 2.750[J]. Enrollment limited.

A. H. Slocum, G. Hom

6.027[J] Biomolecular Feedback Systems

Same subject as 2.180[J]
Subject meets with 2.18[J], 6.557[J]

Prereq: 18.03, Biology (GIR), or permission of instructor
U (Spring)
3-0-9 units

See description under subject 2.180[J].

D. Del Vecchio

6.03 Introduction to EECS via Medical Technology

Prereq: Calculus II (GIR), Physics II (GIR)
U (Spring)
4-4-4 units. Institute LAB

Explores biomedical signals generated from electrocardiograms, glucose detectors or ultrasound images, and magnetic resonance images. Topics include physical characterization and modeling of systems in the time and frequency domains; analog and digital signals and noise; basic machine learning including decision trees, clustering, and classification; and introductory machine vision. Labs designed to strengthen background in signal processing and machine learning. Students design and run structured experiments, and develop and test procedures through further experimentation.

C. M. Stultz, E. Adalsteinsson

6.031 Elements of Software Construction (New)

Prereq: 6.009
U (Spring)
5-0-10 units

Introduces fundamental principles and techniques of software development: how to write software that is safe from bugs, easy to understand, and ready for change. Topics include specifications and invariants; testing, test-case generation, and coverage; abstract data types and representation independence; design patterns for object-oriented programming; concurrent programming, including message passing and shared concurrency, and defending against races and deadlock; and functional programming with immutable data and higher-order functions. Includes weekly programming exercises and larger group programming projects.

M. Goldman, R. C. Miller

6.033 Computer System Engineering

Prereq: 6.004; 6.005 or 6.009
U (Spring)
5-1-6 units

Topics on the engineering of computer software and hardware systems: techniques for controlling complexity; strong modularity using client-server design, operating systems; performance, networks; naming; security and privacy; fault-tolerant systems, atomicity and coordination of concurrent activities, and recovery; impact of computer systems on society. Case studies of working systems and readings from the current literature provide comparisons and contrasts. Includes a single, semester-long design project. Students engage in extensive written communication exercises. Enrollment may be limited.

K. LaCurts, M. F. Kaashoek, H. Balakrishnan

6.034 Artificial Intelligence

Prereq: 6.0001
U (Fall)
4-3-5 units

Introduces representations, methods, and architectures used to build applications and to account for human intelligence from a computational point of view. Covers applications of rule chaining, constraint propagation, constrained search, inheritance, statistical inference, and other problem-solving paradigms. Also addresses applications of identification trees, neural nets, genetic algorithms, support-vector machines, boosting, and other learning paradigms. Considers what separates human intelligence from that of other animals.

P. H. Winston

6.035 Computer Language Engineering

Prereq: 6.004; 6.005 or 6.031
U (Fall)
4-4-4 units

Analyzes issues associated with the implementation of higher-level programming languages. Fundamental concepts, functions, and structures of compilers. The interaction of theory and practice. Using tools in building software. Includes a multi-person project on compiler design and implementation.

M. C. Rinard

6.036 Introduction to Machine Learning

Subject meets with 6.862
Prereq: 6.0001
U (Spring)
4-0-8 units

Introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction; formulation of learning problems; representation, over-fitting, generalization; clustering, classification, probabilistic modeling; and methods such as support vector machines, hidden Markov models, and Bayesian networks. Students taking graduate version complete additional assignments.

R. Barzilay, T. Jaakkola, L. P. Kaelbling

6.037 Structure and Interpretation of Computer Programs

Prereq: None
U (IAP)
1-0-5 units

Studies the structure and interpretation of computer programs which transcend specific programming languages. Demonstrates thought patterns for computer science using Scheme. Includes weekly programming projects. Enrollment may be limited.

Staff

6.041A Introduction to Probability I (New)

Subject meets with 6.431A
Prereq: Calculus II (GIR)
U (Fall, Spring; first half of term)
2-0-4 units

Provides an introduction to probability theory and the modeling and analysis of probabilistic systems. Probabilistic models, conditional probability. Discrete and continuous random variables. Expectation and conditional expectation. Limit Theorems. Students taking graduate version complete additional assignments. Combination of 6.041A and 6.041B counts as a REST subject.

P. Jaillet, J. N. Tsitsiklis

6.041B Introduction to Probability II (New)

Subject meets with 6.431B
Prereq: 6.041A
U (Fall, Spring; second half of term)
2-0-4 units

Building on 6.041A, further discusses topics in probability. Bayesian estimation and hypothesis testing. Elements of statistical inference. Bernoulli and Poisson processes. Markov chains. Students taking graduate version complete additional assignments. Combination of 6.041A and 6.041B counts as a REST subject.

P. Jaillet, J. N. Tsitsiklis

6.042[J] Mathematics for Computer Science

Same subject as 18.062[J]
Prereq: Calculus I (GIR)
U (Fall, Spring)
5-0-7 units. REST

Elementary discrete mathematics for computer science and engineering. Emphasis on mathematical definitions and proofs as well as on applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics include recursive definition and structural induction, state machines and invariants, integer congruences, recurrences, generating functions.

F. T. Leighton, A. R. Meyer, A. Moitra

6.045[J] Automata, Computability, and Complexity

Same subject as 18.400[J]
Prereq: 6.042[J]
U (Spring)
4-0-8 units

Provides an introduction to some of the central ideas of theoretical computer science, including circuits, finite automata, Turing machines and computability, efficient algorithms and reducibility, the P versus NP problem, NP-completeness, the power of randomness, cryptography, computational learning theory, and quantum computing. Examines the classes of problems that can and cannot be solved in various computational models.

S. Aaronson

6.046[J] Design and Analysis of Algorithms

Same subject as 18.410[J]
Prereq: 6.006
U (Fall, Spring)
4-0-8 units

Techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics include sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; greedy algorithms; amortized analysis; graph algorithms; and shortest paths. Advanced topics may include network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing.

E. Demaine, M. Goemans

6.047 Computational Biology: Genomes, Networks, Evolution

Subject meets with 6.878[J], HST.507[J]
Prereq: 6.006, 6.041B, Biology (GIR); or permission of instructor
U (Fall)
3-0-9 units

Covers the algorithmic and machine learning foundations of computational biology, combining theory with practice. Principles of algorithm design, influential problems and techniques, and analysis of large-scale biological datasets. Topics include (a) genomes: sequence analysis, gene finding, RNA folding, genome alignment and assembly, database search; (b) networks: gene expression analysis, regulatory motifs, biological network analysis; (c) evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory. These are coupled with fundamental algorithmic techniques including: dynamic programming, hashing, Gibbs sampling, expectation maximization, hidden Markov models, stochastic context-free grammars, graph clustering, dimensionality reduction, Bayesian networks.

M. Kellis

6.049[J] Evolutionary Biology: Concepts, Models and Computation

Same subject as 7.33[J]
Prereq: 7.03; 6.0001 or permission of instructor
U (Spring)
3-0-9 units

See description under subject 7.33[J].

R. Berwick, D. Bartel

6.050[J] Information, Entropy, and Computation

Same subject as 2.110[J]
Prereq: Physics I (GIR)
U (Spring)
3-0-6 units

Explores the ultimate limits to communication and computation, with an emphasis on the physical nature of information and information processing. Topics include information and computation, digital signals, codes, and compression. Biological representations of information. Logic circuits, computer architectures, and algorithmic information. Noise, probability, and error correction. The concept of entropy applied to channel capacity and to the second law of thermodynamics. Reversible and irreversible operations and the physics of computation. Quantum computation.

P. Penfield, Jr., S. Lloyd

6.057 Introduction to MATLAB

Prereq: None
U (IAP)
1-0-2 units

Accelerated introduction to MATLAB and its popular toolboxes. Lectures are interactive, with students conducting sample MATLAB problems in real time. Includes problem-based MATLAB assignments. Students must provide their own laptop and software. Enrollment limited.

Staff

6.058 Introduction to Signals and Systems, and Feedback Control

Prereq: Calculus II (GIR) or permission of instructor
U (IAP)
2-2-2 units

Introduces fundamental concepts for 6.003, including Fourier and Laplace transforms, convolution, sampling, filters, feedback control, stability, and Bode plots. Students engage in problem solving, using Mathematica and MATLAB software extensively to help visualize processing in the time frequency domains.

Staff

6.061 Introduction to Electric Power Systems

Subject meets with 6.690
Prereq: 6.002, 6.013
Acad Year 2016-2017: U (Spring)
Acad Year 2017-2018: Not offered

3-0-9 units

Electric circuit theory with application to power handling electric circuits. Modeling and behavior of electromechanical devices, including magnetic circuits, motors, and generators. Operational fundamentals of synchronous, induction and DC machinery. Interconnection of generators and motors with electric power transmission and distribution circuits. Power generation, including alternative and sustainable sources. Students taking graduate version complete additional assignments.

J. L. Kirtley, Jr.

6.070[J] Electronics Project Laboratory

Same subject as EC.120[J]
Prereq: None
U (Fall, Spring)
2-2-2 units

Intuition-based introduction to electronics, electronic components and test equipment such as oscilloscopes, meters (voltage, resistance inductance, capacitance, etc.), and signal generators. Emphasizes individual instruction and development of skills, such as soldering, assembly, and troubleshooting. Students design, build, and keep a small electronics project to put their new knowledge into practice. Intended for students with little or no previous background in electronics. Enrollment may be limited.

J. Bales

6.072[J] Introduction to Digital Electronics

Same subject as EC.110[J]
Prereq: None
U (Fall, Spring)
0-3-3 units

See description under subject EC.110[J]. Maximum of 10 students per term, lottery at the first class session if oversubscribed .

J. Bales

6.073[J] Creating Video Games

Same subject as CMS.611[J]
Prereq: 6.01, CMS.301, or CMS.608
U (Spring)
3-3-6 units. HASS-A

See description under subject CMS.611[J]. Limited to 24.

P. Tan, S. Verrilli, R. Eberhardt

6.S062 Special Subject in Electrical Engineering and Computer Science

Prereq: None
U (Fall)
Units arranged
Can be repeated for credit.

Basic undergraduate subjects not offered in the regular curriculum.

Consult Department

6.S063, 6.S064 Special Subject in Electrical Engineering and Computer Science

Prereq: None
U (Fall)
Units arranged
Can be repeated for credit.

Basic undergraduate subjects not offered in the regular curriculum.

Consult Department

6.S08 Special Subject: Interconnected Embedded Systems

Prereq: None
U (Spring)
1-5-6 units. Institute LAB

Introduction to embedded systems in the context of connected devices, wearables and the "Internet of Things". Topics include microcontrollers, energy utilization, algorithmic efficiency, interfacing with sensors, networking, cryptography, local versus distributed computation, data analytics, and 3D printing. Students will design, make, and program an internet-connected wearable device. Final project where student teams will design and demo their own cloud-connected wearable system. Licensed for Spring 2016 by the Committee on Curricula. Enrollment limited; preference to first- and second-year students.

J. Voldman, J. D. Steinmeyer

6.S076-6.S084 Special Subject in Electrical Engineering and Computer Science

Prereq: Permission of instructor
U (Fall)
Units arranged
Can be repeated for credit.

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.

Consult Department

6.S085-6.S099 Special Subject in Electrical Engineering and Computer Science

Prereq: Permission of instructor
U (IAP, Spring)
Not offered regularly; consult department

Units arranged [P/D/F]
Can be repeated for credit.

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.

Consult Department

Undergraduate Laboratory Subjects

6.100 Electrical Engineering and Computer Science Project

Prereq: None
U (Fall, Spring, Summer)
Units arranged
Can be repeated for credit.

Individual experimental work related to electrical engineering and computer science. Student must make arrangements with a project supervisor and file a proposal endorsed by the supervisor. Departmental approval required. Written report to be submitted upon completion of work.

Consult Department Undergraduate Office

6.101 Introductory Analog Electronics Laboratory

Prereq: 6.002 or 6.071
U (Spring)
2-9-1 units. Institute LAB

Introductory experimental laboratory explores the design, construction, and debugging of analog electronic circuits. Lectures and laboratory projects in the first half of the course investigate the performance characteristics of semiconductor devices (diodes, BJTs, and MOSFETs) and functional analog building blocks, including single-stage amplifiers, op amps, small audio amplifier, filters, converters, sensor circuits, and medical electronics (ECG, pulse-oximetry). Projects involve design, implementation, and presentation in an environment similar to that of industry engineering design teams. Instruction and practice in written and oral communication provided. Opportunity to simulate real-world problems and solutions that involve tradeoffs and the use of engineering judgment. Engineers from local companies help students with their design projects.

G. Hom

6.111 Introductory Digital Systems Laboratory

Prereq: 6.002, 6.071, or 16.004
U (Fall)
3-7-2 units. Institute LAB

Lectures and labs on digital logic, flip flops, PALs, FPGAs, counters, timing, synchronization, and finite-state machines prepare students for the design and implementation of a final project of their choice: games, music, digital filters, wireless communications, video, or graphics. Extensive use of Verilog for describing and implementing digital logic designs.

A. P. Chandrakasan, G. P. Hom

6.115 Microcomputer Project Laboratory

Subject meets with 6.1151
Prereq: 6.002, 6.003, 6.004, or 6.007
U (Spring)
3-6-3 units. Institute LAB

Introduces analysis and design of embedded systems. Microcontrollers provide adaptation, flexibility, and real-time control. Emphasizes construction of complete systems, including a five-axis robot arm, a fluorescent lamp ballast, a tomographic imaging station (e.g., a CAT scan), and a simple calculator. Presents a wide range of basic tools, including software and development tools, programmable system on chip, peripheral components such as A/D converters, communication schemes, signal processing techniques, closed-loop digital feedback control, interface and power electronics, and modeling of electromechanical systems. Includes a sequence of assigned projects, followed by a final project of the student's choice, emphasizing creativity and uniqueness. Provides instruction in written and oral communication. Students taking independent inquiry version 6.1151 expand the scope of their laboratory project.

S. B. Leeb

6.1151 Microcomputer Project Laboratory - Independent Inquiry (New)

Subject meets with 6.115
Prereq: 6.002, 6.003, 6.004, or 6.007
U (Spring)
3-9-3 units

Introduces analysis and design of embedded systems. Microcontrollers provide adaptation, flexibility, and real-time control. Emphasizes construction of complete systems, including a five-axis robot arm, a fluorescent lamp ballast, a tomographic imaging station (e.g., a CAT scan), and a simple calculator. Presents a wide range of basic tools, including software and development tools, programmable system on chip, peripheral components such as A/D converters, communication schemes, signal processing techniques, closed-loop digital feedback control, interface and power electronics, and modeling of electromechanical systems. Includes a sequence of assigned projects, followed by a final project of the student's choice, emphasizing creativity and uniqueness. Provides instruction in written and oral communication. Students taking independent inquiry version 6.1151 expand the scope of their laboratory project.

S. B. Leeb

6.117 Introduction to Electrical Engineering Lab Skills

Prereq: None
U (IAP)
1-3-2 units

Introduces basic electrical engineering concepts, components, and laboratory techniques. Covers analog integrated circuits, power supplies, and digital circuits. Lab exercises provide practical experience in constructing projects using multi-meters, oscilloscopes, logic analyzers, and other tools. Includes a project in which students build a circuit to display their own EKG. Enrollment limited.

G. P. Hom

6.123[J] Bioinstrumentation Project Lab

Same subject as 20.345[J]
Prereq: Biology (GIR), and 2.004 or 6.003; or 20.309[J]; or permission of instructor
U (Spring)
2-7-3 units

See description under subject 20.345[J]. Enrollment limited; preference to Course 20 majors and minors.

E. Boyden, M. Jonas, S. F. Nagle, P. So, S. Wasserman, M. F. Yanik

6.129[J] Biological Circuit Engineering Laboratory

Same subject as 20.129[J]
Prereq: Biology (GIR), Calculus II (GIR)
U (Spring)
2-8-2 units. Institute LAB

Students assemble individual genes and regulatory elements into larger-scale circuits; they experimentally characterize these circuits in yeast cells using quantitative techniques, including flow cytometry, and model their results computationally. Emphasizes concepts and techniques to perform independent experimental and computational synthetic biology research. Discusses current literature and ongoing research in the field of synthetic biology. Instruction and practice in oral and written communication provided. Enrollment limited.

T. Lu, R. Weiss

6.131 Power Electronics Laboratory

Subject meets with 6.1311
Prereq: 6.002, 6.003, or 6.007
U (Fall)
3-6-3 units. Institute LAB

Introduces the design and construction of power electronic circuits and motor drives. Laboratory exercises include the construction of drive circuitry for an electric go-cart, flash strobes, computer power supplies, three-phase inverters for AC motors, and resonant drives for lamp ballasts and induction heating. Basic electric machines introduced include DC, induction, and permanent magnet motors, with drive considerations. Provides instruction in written and oral communication. Students taking independent inquiry version 6.1131 expand the scope of their laboratory project.

S. B. Leeb

6.1311 Power Electronics Laboratory - Independent Inquiry (New)

Subject meets with 6.131
Prereq: 6.002, 6.003, or 6.007
U (Fall)
3-9-3 units

Introduces the design and construction of power electronic circuits and motor drives. Laboratory exercises include the construction of drive circuitry for an electric go-cart, flash strobes, computer power supplies, three-phase inverters for AC motors, and resonant drives for lamp ballasts and induction heating. Basic electric machines introduced include DC, induction, and permanent magnet motors, with drive considerations. Provides instruction in written and oral communication. Students taking independent inquiry version 6.1131 expand the scope of their laboratory project.

S. B. Leeb

6.141[J] Robotics: Science and Systems

Same subject as 16.405[J]
Prereq: 1.00 or 6.0001; 2.003[J], 6.005, 6.006, 6.009, or 16.06; or permission of instructor
U (Spring)
2-6-4 units. Institute LAB

Presents concepts, principles, and algorithms for sensing and computation related to the physical world. Topics include motion planning, geometric reasoning, kinematics and dynamics, state estimation, tracking, map building, manipulation, human-robot interaction, fault diagnosis, and embedded system development. Students specify and design a small-scale yet complex robot capable of real-time interaction with the natural world. Students engage in extensive written and oral communication exercises. Enrollment limited.

S. Karaman, D. Rus

6.146 Mobile Autonomous Systems Laboratory: MASLAB

Prereq: None
U (IAP)
2-2-2 units
Can be repeated for credit.

Autonomous robotics contest emphasizing technical AI, vision, mapping and navigation from a robot-mounted camera. Few restrictions are placed on materials, sensors, and/or actuators enabling teams to build robots very creatively. Teams should have members with varying engineering, programming and mechanical backgrounds. Culminates with a robot competition at the end of IAP. Enrollment limited.

Staff

6.147 The Battlecode Programming Competition

Prereq: None
U (IAP)
2-0-4 units
Can be repeated for credit.

Artificial Intelligence programming contest in Java. Student teams program virtual robots to play Battlecode, a real-time strategy game. Competition culminates in a live BattleCode tournament. Assumes basic knowledge of programming.

Staff

6.148 Web Programming Competition

Prereq: Permission of instructor
U (IAP)
1-0-5 units
Can be repeated for credit.

Teams compete to build the most functional and user-friendly website. Competition is judged by industry experts and includes novice and advanced divisions. Prizes awarded. Lectures and workshops cover website basics. Enrollment limited.

Staff

6.149 Introduction to Programming Using Python

Prereq: None
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: U (IAP)

3-0-3 units

Fact-paced introduction to Python programming language for students with little or no programming experience. Covers both function and object-oriented concepts. Includes weekly lab exercises and final project. Enrollment limited.

Staff

6.150 Mobile Applications Competition

Prereq: Permission of instructor
U (IAP)
Not offered regularly; consult department

2-2-2 units
Can be repeated for credit.

Student teams design and build an Android application based on a given theme. Lectures and labs led by experienced students and leading industry experts, covering the basics of Android development, concepts and tools to help participants build great apps. Contest culminates with a public presentation in front of a judging panel comprised of professional developers and MIT faculty. Prizes awarded. Enrollment limited.

Staff

6.151 iOS Game Design and Development Competition

Prereq: None
U (IAP)
2-2-2 units

Introduction to iOS game design and development for students already familiar with object-oriented programming. Provides a set of basic tools (Objective-C and Cocos2D) and exposure to real-world issues in game design. Working in small teams, students complete a final project in which they create their own iPhone game. At the end of IAP, teams present their games in competition for prizes awarded by a judging panel of gaming experts.

Staff

6.152[J] Micro/Nano Processing Technology

Same subject as 3.155[J]
Prereq: Permission of instructor
U (Fall)
3-4-5 units

Introduces the theory and technology of micro/nano fabrication. Lectures and laboratory sessions on basic processing techniques such as vacuum processes, lithography, diffusion, oxidation, and pattern transfer. Students fabricate MOS capacitors, nanomechanical cantilevers, and microfluidic mixers. Emphasis on the interrelationships between material properties and processing, device structure, and the electrical, mechanical, optical, chemical or biological behavior of devices. Provides background for thesis work in micro/nano fabrication. Students engage in extensive written and oral communication exercises.

L. F. Velasquez-Garcia, J. Michel

6.161 Modern Optics Project Laboratory

Subject meets with 6.637
Prereq: 6.003
U (Fall)
3-5-4 units. Institute LAB

Lectures, laboratory exercises and projects on optical signal generation, transmission, detection, storage, processing and display. Topics include polarization properties of light; reflection and refraction; coherence and interference; Fraunhofer and Fresnel diffraction; holography; Fourier optics; coherent and incoherent imaging and signal processing systems; optical properties of materials; lasers and LEDs; electro-optic and acousto-optic light modulators; photorefractive and liquid-crystal light modulation; display technologies; optical waveguides and fiber-optic communication systems; photodetectors. Students may use this subject to find an advanced undergraduate project. Students engage in extensive oral and written communcation exercises. Recommended prerequisites: 6.007 or 8.03.

C. Warde

6.163 Strobe Project Laboratory

Prereq: Physics II (GIR) or permission of instructor
U (Fall, Spring)
2-8-2 units. Institute LAB

Application of electronic flash sources to measurement and photography. First half covers fundamentals of photography and electronic flashes, including experiments on application of electronic flash to photography, stroboscopy, motion analysis, and high-speed videography. Students write four extensive lab reports. In the second half, students work in small groups to select, design, and execute independent projects in measurement or photography that apply learned techniques. Project planning and execution skills are discussed and developed over the term. Students engage in extensive written and oral communication exercises. Enrollment limited.

J. K. Vandiver, J. W. Bales

6.169 Theory and Application of Circuits and Electronics

Prereq: None. Coreq: 6.002
U (Fall, Spring)
1-1-1 units

Building on the framework of 6.002, provides a deeper understanding of the theory and applications of circuits and electronics.

A. Agarwal, J. del Alamo, J. H. Lang, D. J. Perreault

6.170 Software Studio

Prereq: 6.006; 6.005 or 6.031
U (Fall)
4-0-8 units

Covers design and implementation of software systems, using web applications as the platform. Emphasizes the role of conceptual design in achieving clarity, simplicity, and modularity. Students complete open-ended individual assignments and a major team project. Enrollment may be limited.

D. N. Jackson

6.172 Performance Engineering of Software Systems

Subject meets with 6.871
Prereq: 6.004, 6.006; 6.005 or 6.031
U (Fall)
3-12-3 units

Project-based introduction to building efficient, high-performance and scalable software systems. Topics include performance analysis, algorithmic techniques for high performance, instruction-level optimizations, vectorization, cache and memory hierarchy optimization, and parallel programming. Students taking graduate version complete additional assignments.

S. Amarasinghe, C. E. Leiserson

6.175 Constructive Computer Architecture

Prereq: 6.004
U (Fall)
3-8-1 units

Illustrates a constructive (as opposed to a descriptive) approach to computer architecture. Topics include combinational and pipelined arithmetic-logic units (ALU), in-order pipelined microarchitectures, branch prediction, blocking and unblocking caches, interrupts, virtual memory support, cache coherence and multicore architectures. Labs in a modern Hardware Design Language (HDL) illustrate various aspects of microprocessor design, culminating in a term project in which students present a multicore design running on an FPGA board.

Arvind

6.176 Pokerbots Competition

Prereq: None
U (IAP)
1-0-5 units
Can be repeated for credit.

Build autonomous poker players and aquire the knowledge of the game of poker. Showcase decision making skills, apply concepts in mathematics, computer science and economics. Provides instruction in programming, game theory, probability and statistics and machine learning. Concludes with a final competition and prizes. Enrollment limited

Staff

6.177 Building Programming Experience in Python

Prereq: None
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: U (IAP)

1-0-5 units

Preparation for 6.01 aimed to sharpen skills in program design, implementation, and debugging in Python. Programming intensive, with one short structured assignment and a supervised, but highly individual, mandatory project presentation. Intended for students with some elementary programming experience (equivalent to AP Computer Science). Enrollment limited.

Staff

6.178 Introduction to Software Engineering in Java

Prereq: None
U (IAP)
1-1-4 units

Covers the fundamentals of Java, helping students develop intuition about object-oriented programming. Focuses on developing working software that solves real problems. Designed for students with little or no programming experience. Concepts covered useful to 6.005. Enrollment limited.

Staff

6.179 Introduction to C and C++

Prereq: None
U (IAP)
3-3-0 units

Fast-paced introduction to the C and C++ programming languages. Intended for those with experience in other languages who have never used C or C++. Students complete daily assignments, a small-scale individual project, and a mandatory online diagnostic test. Enrollment limited.

Staff

6.182 Psychoacoustics Project Laboratory

Prereq: None
U (Spring)
3-6-3 units. Institute LAB

Introduces the methods used to measure human auditory abilities. Discusses auditory function, principles of psychoacoustic measurement, models for psychoacoustic performance, and experimental techniques. Project topics: absolute and differential auditory sensitivity, operating characteristics of human observers, span of auditory judgment, adaptive measurement procedures, and scaling sensory magnitudes. Knowledge of probability helpful. Students engage in extensive written and oral communication exercises.

L. D. Braida

6.S183-6.S192 Special Laboratory Subject in Electrical Engineering and Computer Science

Prereq: Permission of instructor
U (IAP, Spring)
Not offered regularly; consult department

Units arranged [P/D/F]
Can be repeated for credit.

Laboratory subject that covers content not offered in the regular curriculum. Consult department to learn of offerings for a particular term.

Consult Department

6.S193-6.S198 Special Laboratory Subject in Electrical Engineering and Computer Science

Prereq: Permission of instructor
U (IAP, Spring)
Units arranged
Can be repeated for credit.

Laboratory subject that covers content not offered in the regular curriculum. Consult department to learn of offerings for a particular term.

Consult Department

Senior Projects

6.UAP Undergraduate Advanced Project

Prereq: 6.UAT
U (Fall, IAP, Spring, Summer)
0-6-0 units
Can be repeated for credit.

Research project for those students completing the SB degree, to be arranged by the student and an appropriate MIT faculty member. Students who register for this subject must consult the department undergraduate office. Students engage in extensive written communications exercises.

Consult Department Undergraduate Office

6.UAR Seminar in Undergraduate Advanced Research

Prereq: 6.UR
U (Fall, Spring)
2-0-4 units
Can be repeated for credit.

Instruction in effective undergraduate research, including choosing and developing a research topic, surveying previous work and publications, research topics in EECS, industry best practices, design for robustness, technical presentation, authorship and collaboration, and ethics. Material covered over both fall and spring terms. Students engage in extensive written and oral communication exercises, in the context of an approved advanced research project. May be repeated for credit for a maximum of 12 units.

A. P. Chandrakasan, D. M. Freeman

6.UAT Oral Communication

Prereq: None
U (Fall, Spring)
3-0-6 units

Provides instruction in aspects of effective technical oral presentations and exposure to communication skills useful in a workplace setting. Students create, give and revise a number of presentations of varying length targeting a range of different audiences.

T. L. Eng

6.URS Undergraduate Research in Electrical Engineering and Computer Science

Prereq: Permission of instructor
U (Fall, IAP, Spring, Summer)
Units arranged [P/D/F]
Can be repeated for credit.

Year-long individual research project arranged with appropriate faculty member or approved supervisor. Forms and instructions for the proposal and final report are available in the EECS Undergraduate Office.

A. P. Chandrakasan, D. M. Freeman

Advanced Undergraduate Subjects and Graduate Subjects by Area

Systems Science and Control Engineering

6.207[J] Networks

Same subject as 14.15[J]
Prereq: 6.041B or 14.30
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: U (Spring)

4-0-8 units. HASS-S

See description under subject 14.15[J].

Consult Department Headquarters

6.231 Dynamic Programming and Stochastic Control

Prereq: 6.041B or 18.204; 18.100A, 18.100B, or 18.100Q
G (Spring)
3-0-9 units

Sequential decision-making via dynamic programming. Unified approach to optimal control of stochastic dynamic systems and Markovian decision problems. Applications in linear-quadratic control, inventory control, resource allocation, scheduling, and planning. Optimal decision making under perfect and imperfect state information. Certainty equivalent, open loop-feedback control, rollout, model predictive control, aggregation, and other suboptimal control methods. Infinite horizon problems: discounted, stochastic shortest path, average cost, and semi-Markov models. Value and policy iteration. Abstract models in dynamic programming. Approximate/neurodynamic programming. Simulation based methods. Discussion of current research on the solution of large-scale problems.

J. N. Tsitsiklis

6.241[J] Dynamic Systems and Control

Same subject as 16.338[J]
Prereq: 6.003, 18.06
G (Spring)
4-0-8 units

Linear, discrete- and continuous-time, multi-input-output systems in control, related areas. Least squares and matrix perturbation problems. State-space models, modes, stability, controllability, observability, transfer function matrices, poles and zeros, and minimality. Internal stability of interconnected systems, feedback compensators, state feedback, optimal regulation, observers, and observer-based compensators. Measures of control performance, robustness issues using singular values of transfer functions. Introductory ideas on nonlinear systems. Recommended prerequisite: 6.302.

M. A. Dahleh, A. Megretski, E. Frazzoli

6.245 Multivariable Control Systems

Prereq: 6.241[J] or 16.31
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-0-9 units

Computer-aided design methodologies for synthesis of multivariable feedback control systems. Performance and robustness trade-offs. Model-based compensators; Q-parameterization; ill-posed optimization problems; dynamic augmentation; linear-quadratic optimization of controllers; H-infinity controller design; Mu-synthesis; model and compensator simplification; nonlinear effects. Computer-aided (MATLAB) design homework using models of physical processes.

A. Megretski

6.246, 6.247 Advanced Topics in Control

Prereq: Permission of instructor
G (Spring)
Not offered regularly; consult department

3-0-9 units
Can be repeated for credit.

Advanced study of topics in control. Specific focus varies from year to year.

Consult Department

6.248, 6.249 Advanced Topics in Numerical Methods

Prereq: Permission of instructor
G (Fall, Spring)
Not offered regularly; consult department

3-0-9 units
Can be repeated for credit.

Advanced study of topics in numerical methods. Specific focus varies from year to year.

Consult Department

6.251[J] Introduction to Mathematical Programming

Same subject as 15.081[J]
Prereq: 18.06
G (Fall)
4-0-8 units

Introduction to linear optimization and its extensions emphasizing both methodology and the underlying mathematical structures and geometrical ideas. Covers classical theory of linear programming as well as some recent advances in the field. Topics: simplex method; duality theory; sensitivity analysis; network flow problems; decomposition; integer programming; interior point algorithms for linear programming; and introduction to combinatorial optimization and NP-completeness.

J. N. Tsitsiklis, D. Bertsimas

6.252[J] Nonlinear Optimization

Same subject as 15.084[J]
Prereq: 18.06; 18.100A, 18.100B, or 18.100C
G (Spring)
4-0-8 units

Unified analytical and computational approach to nonlinear optimization problems. Unconstrained optimization methods include gradient, conjugate direction, Newton, sub-gradient and first-order methods. Constrained optimization methods include feasible directions, projection, interior point methods, and Lagrange multiplier methods. Convex analysis, Lagrangian relaxation, nondifferentiable optimization, and applications in integer programming. Comprehensive treatment of optimality conditions and Lagrange multipliers. Geometric approach to duality theory. Applications drawn from control, communications, power systems, and resource allocation problems.

R. M. Freund, D. P. Bertsekas, G. Perakis

6.253 Convex Analysis and Optimization

Prereq: 18.06; 18.100A, 18.100B, or 18.100C
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

3-0-9 units

Core analytical issues of continuous optimization, duality, and saddle point theory, and development using a handful of unifying principles that can be easily visualized and readily understood. Discusses in detail the mathematical theory of convex sets and functions which are the basis for an intuitive, highly visual, geometrical approach to the subject. Convex optimization algorithms focus on large-scale problems, drawn from several types of applications, such as resource allocation and machine learning. Includes batch and incremental subgradient, cutting plane, proximal, and bundle methods.

D. P. Bertsekas

6.254 Game Theory with Engineering Applications

Prereq: 6.041B
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

4-0-8 units

Introduction to fundamentals of game theory and mechanism design with motivations for each topic drawn from engineering applications (including distributed control of wireline/wireless communication networks, transportation networks, pricing). Emphasis on the foundations of the theory, mathematical tools, as well as modeling and the equilibrium notion in different environments. Topics include normal form games, supermodular games, dynamic games, repeated games, games with incomplete/imperfect information, mechanism design, cooperative game theory, and network games.

A. Ozdaglar

6.255[J] Optimization Methods

Same subject as 15.093[J], IDS.200[J]
Prereq: 18.06
G (Fall)
4-0-8 units

See description under subject 15.093[J].

D. Bertsimas, P. Parrilo

6.256 Algebraic Techniques and Semidefinite Optimization

Prereq: 6.251[J] or 6.255[J]
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

3-0-9 units

Theory and computational techniques for optimization problems involving polynomial equations and inequalities with particular, emphasis on the connections with semidefinite optimization. Develops algebraic and numerical approaches of general applicability, with a view towards methods that simultaneously incorporate both elements, stressing convexity-based ideas, complexity results, and efficient implementations. Examples from several engineering areas, in particular systems and control applications. Topics include semidefinite programming, resultants/discriminants, hyperbolic polynomials, Groebner bases, quantifier elimination, and sum of squares.

P. Parrilo

6.260, 6.261 Advanced Topics in Communications

Prereq: Permission of instructor
G (Fall, Spring)
Not offered regularly; consult department

3-0-9 units
Can be repeated for credit.

Advanced study of topics in communications. Specific focus varies from year to year.

Consult Department

6.262 Discrete Stochastic Processes

Prereq: 6.041B, 6.431B or 18.204
G (Spring)
4-0-8 units

Review of probability and laws of large numbers; Poisson counting process and renewal processes; Markov chains (including Markov decision theory), branching processes, birth-death processes, and semi-Markov processes; continuous-time Markov chains and reversibility; random walks, martingales, and large deviations; applications from queueing, communication, control, and operations research.

R. G. Gallager, V. W. S. Chan

6.263[J] Data-Communication Networks

Same subject as 16.37[J]
Prereq: 6.041B or 18.204
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-0-9 units

Provides an introduction to data networks with an analytic perspective, using telephone networks, wireless networks, optical networks, the Internet and data centers as primary applications. Presents basic tools for modeling and performance analysis accompanied by elementary, meaningful simulations. Develops insights for large networks by means of simple approximations. Draws upon concepts from queueing theory and optimization.

E. Modiano, D. Shah

6.265[J] Advanced Stochastic Processes

Same subject as 15.070[J]
Prereq: 6.431B, 15.085[J], 18.100A, 18.100B, or 18.100Q
G (Spring)
3-0-9 units

See description under subject 15.070[J].

D. Gamarnik, G. Bresler

6.267 Heterogeneous Networks: Architecture, Transport, Proctocols, and Management

Prereq: 6.041B or 6.042[J]
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

4-0-8 units

Introduction to modern heterogeneous networks and the provision of heterogeneous services. Architectural principles, analysis, algorithmic techniques, performance analysis, and existing designs are developed and applied to understand current problems in network design and architecture. Begins with basic principles of networking. Emphasizes development of mathematical and algorithmic tools; applies them to understanding network layer design from the performance and scalability viewpoint. Concludes with network management and control, including the architecture and performance analysis of interconnected heterogeneous networks. Provides background and insight to understand current network literature and to perform research on networks with the aid of network design projects.

V. W. S. Chan, R. G. Gallager

6.268 Network Science and Models

Prereq: 6.041B, 18.06
Acad Year 2016-2017: G (Spring)
Acad Year 2017-2018: Not offered

3-0-9 units

Introduces the main mathematical models used to describe large networks and dynamical processes that evolve on networks. Static models of random graphs, preferential attachment, and other graph evolution models. Epidemic propagation, opinion dynamics, social learning, and inference in networks. Applications drawn from social, economic, natural, and infrastructure networks, as well as networked decision systems such as sensor networks.

J. N. Tsitsiklis, P. Jaillet

Electronics, Computers, and Systems

6.301 Solid-State Circuits

Prereq: 6.012
U (Fall)
3-2-7 units

Analysis and design of transistor circuits, based directly on the semiconductor physics and transistor circuit models developed in 6.012. High-frequency and low-frequency design calculations and simulation of multistage transistor circuits. Trans-linear circuits. Introduction to operational-amplifier design and application. Some previous laboratory experience assumed.

H. S. Lee

6.302 Feedback System Design

Subject meets with 6.320
Prereq: 6.003, 2.003[J], or 16.002
U (Spring)
4-2-6 units

Learn-by-design introduction to continuous and discrete-time system modeling and feedback control. Topics include performance metrics; time- and frequency-domain model extraction and classical control; and basic state-space control. Students apply the control concepts in weekly labs and in a midterm project. Labs involve designing circuits and software, and using sensors and a high-performance microcontroller, to address control problems, such as positioning a motor- or propeller-actuated robot arm, reducing distortion in a PWM-based audio amplifier, eliminating field crosstalk for a magnetic-resonance imager, stabilizing magnetic levitation, balancing a two-wheel vehicle. Students taking graduate version complete additional assignments and an extra lab on observer-based state-space control. Intended for students who have previous laboratory experience with electronic systems.

J. D. Steinmeyer, J. K. White

6.320 Feedback System Design (New)

Subject meets with 6.302
Prereq: 6.003, 2.004, 2.04A, or 16.002
G (Spring)
4-2-6 units

Learn-by-design introduction to continuous and discrete-time system modeling and feedback control. Topics include performance metrics; time- and frequency-domain model extraction and classical control; and basic state-space control. Students apply the control concepts in weekly labs and in a midterm project. Labs involve designing circuits and software, and using sensors and a high-performance microcontroller, to address control problems, such as positioning a motor- or propeller-actuated robot arm, reducing distortion in a PWM-based audio amplifier, eliminating field crosstalk for a magnetic-resonance imager, stabilizing magnetic levitation, balancing a two-wheel vehicle. Students taking graduate version complete additional assignments and an extra lab on observer-based state-space control. Intended for students who have previous laboratory experience with electronic systems. students taking graduate version complete additional assignments.

J. D. Steinmeyer, J. K. White

6.332, 6.333 Advanced Topics in Circuits

Prereq: Permission of instructor
G (Fall, Spring)
Not offered regularly; consult department

3-0-9 units
Can be repeated for credit.

Advanced study of topics in circuits. Specific focus varies from year to year. Consult department for details.

Consult Department

6.334 Power Electronics

Prereq: 6.012
G (Spring)
3-0-9 units

The application of electronics to energy conversion and control. Modeling, analysis, and control techniques. Design of power circuits including inverters, rectifiers, and dc-dc converters. Analysis and design of magnetic components and filters. Characteristics of power semiconductor devices. Numerous application examples, such as motion control systems, power supplies, and radio-frequency power amplifiers.

D. J. Perreault

6.335[J] Fast Methods for Partial Differential and Integral Equations

Same subject as 18.336[J]
Prereq: 6.336[J], 16.920[J], 18.085, 18.335[J], or permission of instructor
G (Fall)
3-0-9 units

See description under subject 18.336[J].

C. Perez

6.336[J] Introduction to Numerical Simulation

Same subject as 2.096[J], 16.910[J]
Prereq: 18.03 or 18.06
G (Fall)
3-3-6 units

Introduction to computational techniques for the simulation of a large variety of engineering and physical systems. Applications are drawn from aerospace, mechanical, electrical, chemical engineering, biology, and materials science. Topics include mathematical formulations (techniques for automatic assembly of mathematical problems from physics' principles); sparse, direct and iterative solution techniques for linear systems; Newton and Homotopy methods for nonlinear problems; discretization methods for ordinary, time-periodic and partial differential equations; accelerated methods for integral equations; techniques for automatic generation of compact dynamical system models and model order reduction.

L. Daniel, J. K. White

6.337[J] Introduction to Numerical Methods

Same subject as 18.335[J]
Prereq: 18.06, 18.700, or 18.701
G (Spring)
3-0-9 units

See description under subject 18.335[J].

W. Shin

6.338[J] Parallel Computing

Same subject as 18.337[J]
Prereq: 18.06, 18.700, or 18.701
G (Fall)
3-0-9 units

See description under subject 18.337[J].

A. Edelman

6.339[J] Numerical Methods for Partial Differential Equations

Same subject as 2.097[J], 16.920[J]
Prereq: 18.03 or 18.06
G (Fall)
3-0-9 units

See description under subject 16.920[J].

Q. Wang, J. K. White

6.341 Discrete-Time Signal Processing

Prereq: 6.011
G (Fall)
4-0-8 units

Representation, analysis, and design of discrete time signals and systems. Decimation, interpolation, and sampling rate conversion. Noise shaping. Flowgraph structures for DT systems. Lattice filters. Time- and frequency-domain design techniques for IIR and FIR filters. Parametric signal modeling, linear prediction, and the relation to lattice filters. Discrete Fourier transform (DFT). Computation of the DFT including FFT algorithms. Short-time Fourier analysis and relation to filter banks. Multirate techniques. Perfect reconstruction filter banks and their relation to wavelets.

A. V. Oppenheim, J. Ward

6.344 Digital Image Processing

Prereq: 6.003, 6.041B
G (Spring)
3-0-9 units

Digital images as two-dimensional signals. Digital signal processing theories used for digital image processing, including one-dimensional and two-dimensional convolution, Fourier transform, discrete Fourier transform, and discrete cosine transform. Image processing basics. Image enhancement. Image restoration. Image coding and compression. Video processing including video coding and compression. Additional topics including digital high-definition television systems.

J. S. Lim

6.345[J] Automatic Speech Recognition

Same subject as HST.728[J]
Prereq: 6.011, 6.036
Acad Year 2016-2017: G (Spring)
Acad Year 2017-2018: Not offered

3-1-8 units

Introduces the rapidly developing fields of automatic speech recognition and spoken language processing. Topics include acoustic theory of speech production, acoustic-phonetics, signal representation, acoustic and language modeling, search, hidden Markov modeling, deep neural networks, system robustness, adaptation, and other related speech processing topics. Lecture material intersperses theory with practice. Includes problem sets, laboratory exercises, and opened-ended term project.

V. W. Zue, J. R. Glass

6.347, 6.348 Advanced Topics in Signals and Systems

Prereq: Permission of instructor
G (Fall, Spring)
Not offered regularly; consult department

3-0-9 units
Can be repeated for credit.

Advanced study of topics in signals and systems. Specific focus varies from year to year.

Consult Department

6.374 Analysis and Design of Digital Integrated Circuits

Prereq: 6.012, 6.004
G (Fall)
3-3-6 units

Device and circuit level optimization of digital building blocks. MOS device models including Deep Sub-Micron effects. Circuit design styles for logic, arithmetic, and sequential blocks. Estimation and minimization of energy consumption. Interconnect models and parasitics, device sizing and logical effort, timing issues (clock skew and jitter), and active clock distribution techniques. Memory architectures, circuits (sense amplifiers), and devices. Testing of integrated circuits. Extensive custom and standard cell layout and simulation in design projects and software labs.

V. Sze, A. P. Chandrakasan

6.375 Complex Digital Systems Design

Prereq: 6.004
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

5-5-2 units

Introduction to the design and implementation of large-scale digital systems using hardware description languages and high-level synthesis tools in conjunction with standard commercial electronic design automation (EDA) tools. Emphasizes modular and robust designs, reusable modules, correctness by construction, architectural exploration, meeting area and timing constraints, and developing functional field-programmable gate array (FPGA) prototypes. Extensive use of CAD tools in weekly labs serve as preparation for a multi-person design project on multi-million gate FPGAs. Enrollment may be limited.

Arvind

Probabilistic Systems and Communication

6.431A Introduction to Probability I (New)

Subject meets with 6.041A
Prereq: Calculus II (GIR)
G (Fall, Spring; first half of term)
2-0-4 units

Provides an introduction to probability theory and the modeling and analysis of probabilistic systems. Probabilistic models, conditional probability. Discrete and continuous random variables. Expectation and conditional expectation. Limit Theorems. Students taking graduate version complete additional assignments.

P. Jaillet, J. N. Tsitsiklis

6.431B Introduction to Probability II (New)

Subject meets with 6.041B
Prereq: 6.431A
G (Fall, Spring; second half of term)
2-0-4 units

Further topics in probability. Bayesian estimation and hypothesis testing. Elements of statistical inference. Bernoulli and Poisson processes. Markov chains. Students taking graduate version complete additional assignments.

P. Jaillet, J. N. Tsitsiklis

6.434[J] Statistics for Engineers and Scientists

Same subject as 16.391[J]
Prereq: Calculus II (GIR), 18.06, 6.431B, or permission of instructor
G (Fall)
3-0-9 units

Rigorous introduction to fundamentals of statistics motivated by engineering applications. Topics include exponential families, order statistics, sufficient statistics, estimation theory, hypothesis testing, measures of performance, notions of optimality, analysis of variance (ANOVA), simple linear regression, and selected topics.

M. Win, J. N. Tsitsiklis

6.436[J] Fundamentals of Probability

Same subject as 15.085[J]
Prereq: Calculus II (GIR)
G (Fall)
4-0-8 units

Introduction to probability theory. Probability spaces and measures. Discrete and continuous random variables. Conditioning and independence. Multivariate normal distribution. Abstract integration, expectation, and related convergence results. Moment generating and characteristic functions. Bernoulli and Poisson process. Finite-state Markov chains. Convergence notions and their relations. Limit theorems. Familiarity with elementary notions in probability and real analysis is desirable.

J. N. Tsitsiklis, D. Gamarnik

6.437 Inference and Information

Prereq: 6.008, 6.041B, or 6.436[J]
G (Spring)
4-0-8 units

Introduction to principles of Bayesian and non-Bayesian statistical inference. Hypothesis testing and parameter estimation, sufficient statistics; exponential families. EM agorithm. Log-loss inference criterion, entropy and model capacity. Kullback-Leibler distance and information geometry. Asymptotic analysis and large deviations theory. Model order estimation; nonparametric statistics. Computational issues and approximation techniques; Monte Carlo methods. Selected special topics such as universal prediction and compression.

P. Golland, G. W. Wornell

6.438 Algorithms for Inference

Prereq: 6.008, 6.041B, or 6.436[J]; 18.06
G (Fall)
4-0-8 units

Introduction to statistical inference with probabilistic graphical models. Directed and undirected graphical models, and factor graphs, over discrete and Gaussian distributions; hidden Markov models, linear dynamical systems. Sum-product and junction tree algorithms; forward-backward algorithm, Kalman filtering and smoothing. Min-sum and Viterbi algorithms. Variational methods, mean-field theory, and loopy belief propagation. Particle methods and filtering. Building graphical models from data, including parameter estimation and structure learning; Baum-Welch and Chow-Liu algorithms. Selected special topics.

P. Golland, G. W. Wornell, D. Shah

6.440 Essential Coding Theory

Prereq: 6.006, 6.045[J]
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

3-0-9 units

Introduces the theory of error-correcting codes. Focuses on the essential results in the area, taught from first principles. Special focus on results of asymptotic or algorithmic significance. Principal topics include construction and existence results for error-correcting codes; limitations on the combinatorial performance of error-correcting codes; decoding algorithms; and applications to other areas of mathematics and computer science.

Staff

6.441 Information Theory

Prereq: 6.041B
G (Spring)
3-0-9 units

Mathematical definitions of information measures, convexity, continuity, and variational properties. Lossless source coding; variable-length and block compression; Slepian-Wolf theorem; ergodic sources and Shannon-McMillan theorem. Hypothesis testing, large deviations and I-projection. Fundamental limits of block coding for noisy channels: capacity, dispersion, finite blocklength bounds. Coding with feedback. Joint source-channel problem. Rate-distortion theory, vector quantizers. Advanced topics include Gelfand-Pinsker problem, multiple access channels, broadcast channels (depending on available time).

M. Medard, Y. Polyanskiy, L. Zheng

6.442 Optical Networks

Prereq: 6.041B or 6.042[J]
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

3-0-9 units

Introduces the fundamental and practical aspects of optical network technology, architecture, design and analysis tools and techniques. The treatment of optical networks are from the architecture and system design points of view. Optical hardware technologies are introduced and characterized as fundamental network building blocks on which optical transmission systems and network architectures are based. Beyond the Physical Layer, the higher network layers (Media Access Control, Network and Transport Layers) are treated together as integral parts of network design. Performance metrics, analysis and optimization techniques are developed to help guide the creation of high performance complex optical networks.

V. W. S. Chan

6.443[J] Quantum Information Science

Same subject as 8.371[J], 18.436[J]
Prereq: 18.435[J]
G (Spring)
3-0-9 units

See description under subject 8.371[J].

I. Chuang

6.450 Principles of Digital Communication

Prereq: 6.011
G (Fall)
3-0-9 units

Communication sources and channels; data compression; entropy and the AEP; Lempel-Ziv universal coding; scalar and vector quantization; L2 waveforms; signal space and its representation by sampling and other expansions; aliasing; the Nyquist criterion; PAM and QAM modulation; Gaussian noise and random processes; detection and optimal receivers; fading channels and wireless communication; introduction to communication system design.

M. Medard, L. Zheng

6.452 Principles of Wireless Communication

Prereq: 6.450
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-0-9 units

Introduction to design, analysis, and fundamental limits of wireless transmission systems. Wireless channel and system models; fading and diversity; resource management and power control; multiple-antenna and MIMO systems; space-time codes and decoding algorithms; multiple-access techniques and multiuser detection; broadcast codes and precoding; cellular and ad-hoc network topologies; OFDM and ultrawideband systems; architectural issues.

G. W. Wornell, L. Zheng

6.453 Quantum Optical Communication

Prereq: 6.011, 18.06
G (Fall)
3-0-9 units

Quantum optics: Dirac notation quantum mechanics; harmonic oscillator quantization; number states, coherent states, and squeezed states; radiation field quantization and quantum field propagation; P-representation and classical fields. Linear loss and linear amplification: commutator preservation and the Uncertainty Principle; beam splitters; phase-insensitive and phase-sensitive amplifiers. Quantum photodetection: direct detection, heterodyne detection, and homodyne detection. Second-order nonlinear optics: phasematched interactions; optical parametric amplifiers; generation of squeezed states, photon-twin beams, non-classical fourth-order interference, and polarization entanglement. Quantum systems theory: optimum binary detection; quantum precision measurements; quantum cryptography; and quantum teleportation.

J. H. Shapiro

6.454 Graduate Seminar in Area I

Prereq: Permission of instructor
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

2-0-4 units
Can be repeated for credit.

Student-run advanced graduate seminar with focus on topics in communications, control, signal processing, optimization. Participants give presentations outside of their own research to expose colleagues to topics not covered in the usual curriculum. Recent topics have included compressed sensing, MDL principle, communication complexity, linear programming decoding, biology in EECS, distributed hypothesis testing, algorithms for random satisfaction problems, and cryptogaphy. Open to advanced students from all areas of EECS. Limited to 12.

L. Zheng, D. Shah

6.456 Array Processing

Prereq: 6.341; 2.687, or 6.011 and 18.06
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-2-7 units

Adaptive and non-adaptive processing of signals received at arrays of sensors. Deterministic beamforming, space-time random processes, optimal and adaptive algorithms, and the sensitivity of algorithm performance to modeling errors and limited data. Methods of improving the robustness of algorithms to modeling errors and limited data are derived. Advanced topics include an introduction to matched field processing and physics-based methods of estimating signal statistics. Homework exercises providing the opportunity to implement and analyze the performance of algorithms in processing data supplied during the course.

Staff

Bioelectrical Engineering

6.503 Foundations of Algorithms and Computational Techniques in Systems Biology

Subject meets with 6.581[J], 20.482[J]
Prereq: 6.021[J], 6.034, 6.046[J], 6.336[J], 18.417, or permission of instructor
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: U (Spring)

3-0-9 units

Illustrates computational approaches to solving problems in systems biology. Uses a series of case studies to demonstrate how an effective match between the statement of a biological problem and the selection of an appropriate algorithm or computational technique can lead to fundamental advances. Covers several discrete and numerical algorithms used in simulation, feature extraction, and optimization for molecular, network, and systems models in biology. Students taking graduate version complete additional assignments.

B. Tidor, J. K. White

6.521[J] Cellular Neurophysiology

Same subject as 2.794[J], 20.470[J], HST.541[J]
Subject meets with 2.791[J], 6.021[J], 20.370[J]

Prereq: Physics II (GIR); 18.03; 2.005, 6.002, 6.003, 6.071, 10.301, 20.110[J], or permission of instructor
G (Fall)
5-2-5 units

Meets with undergraduate subject 6.021[J]. Requires the completion of more advanced home problems and/or an additional project.

J. Han, T. Heldt

6.522[J] Quantitative Physiology: Organ Transport Systems

Same subject as 2.796[J]
Subject meets with 2.792[J], 6.022[J], HST.542[J]

Prereq: 2.006 or 6.013; 6.021[J]
G (Spring)
4-2-6 units

Application of the principles of energy and mass flow to major human organ systems. Mechanisms of regulation and homeostasis. Anatomical, physiological and pathophysiological features of the cardiovascular, respiratory and renal systems. Systems, features and devices that are most illuminated by the methods of physical sciences. Laboratory work includes some animal studies. Students taking graduate version complete additional assignments.

T. Heldt, R. G. Mark, C. M. Stultz

6.524[J] Molecular, Cellular, and Tissue Biomechanics

Same subject as 2.798[J], 3.971[J], 10.537[J], 20.410[J]
Prereq: Biology (GIR); 2.002, 2.006, 6.013, 10.301, or 10.302
G (Fall)
3-0-9 units

See description under subject 20.410[J].

R. D. Kamm, K. J. Van Vliet

6.525[J] Medical Device Design

Same subject as 2.75[J], HST.552[J]
Subject meets with 2.750[J], 6.025[J]

Prereq: 2.72, 6.101, 6.111, 6.115, 22.071, or permission of instructor
G (Fall)
3-0-9 units

See description under subject 2.75[J]. Enrollment limited.

A. H. Slocum, G. Hom

6.542[J] Laboratory on the Physiology, Acoustics, and Perception of Speech

Same subject as 24.966[J], HST.712[J]
Prereq: Permission of instructor
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

2-2-8 units

Experimental investigations of speech processes. Topics: measurement of articulatory movements; measurements of pressures and airflows in speech production; computer-aided waveform analysis and spectral analysis of speech; synthesis of speech; perception and discrimination of speechlike sounds; speech prosody; models for speech recognition; speech development; and other topics. Recommended prerequisites: 6.002 or 18.03.

L. D. Braida, S. Shattuck-Hufnagel

6.544, 6.545 Advanced Topics in BioEECS

Prereq: Permission of instructor
G (Fall, Spring)
Not offered regularly; consult department

3-0-9 units
Can be repeated for credit.

Advanced study of topics in BioEECS. Specific focus varies from year to year. Consult department for details.

Consult Department

6.552[J] Signal Processing by the Auditory System: Perception

Same subject as HST.716[J]
Prereq: 6.003; 6.041B or 6.431B; or permission of instructor
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-0-9 units

Studies information processing performance of the human auditory system in relation to current physiological knowledge. Examines mathematical models for the quantification of auditory-based behavior and the relation between behavior and peripheral physiology, reflecting the tono-topic organization and stochastic responses of the auditory system. Mathematical models of psychophysical relations, incorporating quantitative knowledge of physiological transformations by the peripheral auditory system.

L. D. Braida

6.555[J] Biomedical Signal and Image Processing

Same subject as 16.456[J], HST.582[J]
Prereq: 6.003, 2.004, 16.002, or 18.085
G (Spring)
3-3-6 units

See description under subject HST.582[J].

J. Greenberg, E. Adalsteinsson, W. Wells

6.556[J] Data Acquisition and Image Reconstruction in MRI

Same subject as HST.580[J]
Prereq: 6.011
G (Fall)
3-0-9 units

Applies analysis of signals and noise in linear systems, sampling, and Fourier properties to magnetic resonance (MR) imaging acquisition and reconstruction. Provides adequate foundation for MR physics to enable study of RF excitation design, efficient Fourier sampling, parallel encoding, reconstruction of non-uniformly sampled data, and the impact of hardware imperfections on reconstruction performance. Surveys active areas of MR research. Assignments include Matlab-based work with real data. Includes visit to a scan site for human MR studies.

E. Adalsteinsson

6.557[J] Biomolecular Feedback Systems

Same subject as 2.18[J]
Subject meets with 2.180[J], 6.027[J]

Prereq: 18.03, Biology (GIR), or permission of instructor
G (Spring)
3-0-9 units

See description under subject 2.18[J].

D. Del Vecchio, R. Weiss

6.561[J] Fields, Forces, and Flows in Biological Systems

Same subject as 2.795[J], 10.539[J], 20.430[J]
Prereq: 6.013, 2.005, 10.302, or permission of instructor
G (Fall)
3-0-9 units

See description under subject 20.430[J].

M. Bathe, A. J. Grodzinsky

6.580[J] Principles of Synthetic Biology

Same subject as 20.305[J]
Subject meets with 6.589[J], 20.405[J]

Prereq: None
U (Fall)
3-0-9 units

See description under subject 20.305[J].

R. Weiss

6.581[J] Foundations of Algorithms and Computational Techniques in Systems Biology

Same subject as 20.482[J]
Subject meets with 6.503

Prereq: 6.021[J], 6.034, 6.046[J], 6.336[J], 18.417, or permission of instructor
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

3-0-9 units

Illustrates computational approaches to solving problems in systems biology. Uses a series of case studies to demonstrate how an effective match between the statement of a biological problem and the selection of an appropriate algorithm or computational technique can lead to fundamental advances. Covers several discrete and numerical algorithms used in simulation, feature extraction, and optimization for molecular, network, and systems models in biology. Students taking graduate version complete additional assignments.

B. Tidor, J. K. White

6.589[J] Principles of Synthetic Biology

Same subject as 20.405[J]
Subject meets with 6.580[J], 20.305[J]

Prereq: None
G (Fall)
3-0-9 units

See description under subject 20.405[J].

R. Weiss

Electrodynamics

6.602 Fundamentals of Photonics

Subject meets with 6.621
Prereq: 2.71, 6.013, or 8.07
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: U (Fall)

3-0-9 units

Covers the fundamentals of optics and the interaction of light and matter, leading to devices such as light emitting diodes, optical amplifiers, and lasers. Topics include classical ray, wave, beam, and Fourier optics; Maxwell's electromagnetic waves; resonators; quantum theory of photons; light-matter interaction; laser amplification; lasers; and semiconductors optoelectronics. Students taking graduate version complete additional assignments.

D. R. Englund

6.621 Fundamentals of Photonics

Subject meets with 6.602
Prereq: 2.71, 6.013, or 8.07
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-0-9 units

Covers the fundamentals of optics and the interaction of light and matter, leading to devices such as light emitting diodes, optical amplifiers, and lasers. Topics include classical ray, wave, beam, and Fourier optics; Maxwell's electromagnetic waves; resonators; quantum theory of photons; light-matter interaction; laser amplification; lasers; and semiconductors optoelectronics. Students taking graduate version complete additional assignments.

D. R. Englund

6.630 Electromagnetics

Prereq: 6.003 or 6.007
G (Fall)
4-0-8 units

Explores electromagnetic phenomena in modern applications, including wireless and optical communications, circuits, computer interconnects and peripherals, microwave communications and radar, antennas, sensors, micro-electromechanical systems, and power generation and transmission. Fundamentals include quasistatic and dynamic solutions to Maxwell's equations; waves, radiation, and diffraction; coupling to media and structures; guided and unguided waves; modal expansions; resonance; acoustic analogs; and forces, power, and energy.

L. Daniel, M. R. Watts

6.631 Optics and Photonics

Prereq: 6.013 or 8.07
G (Fall)
3-0-9 units

Introduction to fundamental concepts and techniques of optics, photonics, and fiber optics. Review of Maxwell's equations, light propagation, and reflection from dielectrics mirrors and filters. Interferometers, filters, and optical imaging systems. Fresnel and Fraunhoffer diffraction theory. Propagation of Gaussian beams and laser resonator design. Optical waveguides and optical fibers. Optical waveguide and photonic devices.

J. G. Fujimoto

6.632 Electromagnetic Wave Theory

Prereq: 6.013, 6.630, or 8.07
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

3-0-9 units

Solutions to Maxwell equations and physical interpretation. Topics include waves in media, equivalence principle, duality and complementarity, Huygens' principle, Fresnel and Fraunhofer diffraction, radiation and dyadic Green's functions, scattering, metamaterials, and plasmonics, mode theory, dielectric waveguides, and resonators. Examples deal with limiting cases of electromagnetic theory, multi-port elements, filters and antennas. Discusses current topics in microwave and photonic devices.

M. R. Watts

6.634[J] Nonlinear Optics

Same subject as 8.431[J]
Prereq: 6.013 or 8.07
G (Spring)
3-0-9 units

Techniques of nonlinear optics with emphasis on fundamentals for research and engineering in optics, photonics, and spectroscopy. Electro optic modulators, harmonic generation, and frequency conversion devices. Nonlinear effects in optical fibers including self-phase modulation, nonlinear wave propagation, and solitons. Interaction of light with matter, laser operation, density matrix techniques, nonlinear spectroscopies, and femtosecond optics.

J. G. Fujimoto

6.637 Optical Signals, Devices, and Systems

Subject meets with 6.161
Prereq: 6.003
G (Fall)
3-0-9 units

Principles of operation and applications of devices and systems for optical signal generation, transmission, detection, storage, processing and display. Topics include review of the basic properties of electromagnetic waves; coherence and interference; diffraction and holography; Fourier optics; coherent and incoherent imaging and signal processing systems; optical properties of materials; lasers and LEDs; electro-optic and acousto-optic light modulators; photorefractive and liquid-crystal light modulation; spatial light modulators and displays; optical waveguides and fiber-optic communication systems; photodetectors; 2-D and 3-D optical storage technologies; adaptive optical systems; role of optics in next-generation computers. Student research paper on a specific contemporary topic required. Recommended prerequisites: 6.007 or 8.03.

C. Warde

6.641 Electromagnetic Fields, Forces, and Motion

Prereq: 6.013
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

4-0-8 units

Electric and magnetic quasistatic forms of Maxwell's equations applied to dielectric, conduction, and magnetization boundary value problems. Electromagnetic forces, force densities, and stress tensors, including magnetization and polarization. Thermodynamics of electromagnetic fields, equations of motion, and energy conservation. Applications to synchronous, induction, and commutator machines; sensors and transducers; microelectromechanical systems; propagation and stability of electromechanical waves; and charge transport phenomena.

J. H. Lang

6.642 Continuum Electromechanics

Prereq: 6.641 or permission of instructor
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

4-0-8 units

Laws, approximations, and relations of continuum mechanics. Mechanical and electromechanical transfer relations. Statics and dynamics of electromechanical systems having a static equilibrium. Electromechanical flows. Field coupling with thermal and molecular diffusion. Electrokinetics. Streaming interactions. Application to materials processing, magnetohydrodynamic and electrohydrodynamic pumps and generators, ferrohydrodynamics, physiochemical systems, heat transfer, continuum feedback control, electron beam devices, and plasma dynamics.

Staff

6.644, 6.645 Advanced Topics in Applied Physics

Prereq: Permission of instructor
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-0-9 units
Can be repeated for credit.

Advanced study of topics in applied physics. Specific focus varies from year to year. Consult department for details.

Consult Department

6.685 Electric Machines

Prereq: 6.061 or 6.690; or permission of instructor
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-0-9 units

Treatment of electromechanical transducers, rotating and linear electric machines. Lumped-parameter electromechanics. Power flow using Poynting's theorem, force estimation using the Maxwell stress tensor and Principle of virtual work. Development of analytical techniques for predicting device characteristics: energy conversion density, efficiency; and of system interaction characteristics: regulation, stability, controllability, and response. Use of electric machines in drive systems. Problems taken from current research.

J. L. Kirtley, Jr.

6.690 Introduction to Electric Power Systems

Subject meets with 6.061
Prereq: 6.002, 6.013
Acad Year 2016-2017: G (Spring)
Acad Year 2017-2018: Not offered

3-0-9 units

Electric circuit theory with application to power handling electric circuits. Modeling and behavior of electromechanical devices, including magnetic circuits, motors, and generators. Operational fundamentals of synchronous, induction and DC machinery. Interconnection of generators and motors with electric power transmission and distribution circuits. Power generation, including alternative and sustainable sources. Students taking graduate version complete additional assignments.

J. L. Kirtley, Jr.

6.695[J] Engineering, Economics and Regulation of the Electric Power Sector

Same subject as 15.032[J], IDS.505[J]
Prereq: Permission of instructor
G (Spring)
3-0-9 units

See description under subject IDS.505[J].

C. Vergara

Solid-State Materials and Devices

6.701 Introduction to Nanoelectronics

Subject meets with 6.719
Prereq: 6.003
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: U (Fall)

4-0-8 units

Transistors at the nanoscale. Quantization, wavefunctions, and Schrodinger's equation. Introduction to electronic properties of molecules, carbon nanotubes, and crystals. Energy band formation and the origin of metals, insulators and semiconductors. Ballistic transport, Ohm's law, ballistic versus traditional MOSFETs, fundamental limits to computation.

M. A. Baldo

6.717[J] Design and Fabrication of Microelectromechanical Systems

Same subject as 2.374[J]
Subject meets with 2.372[J], 6.777[J]

Prereq: 6.003 or 2.003[J], Physics II (GIR); or permission of instructor
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: U (Spring)

3-0-9 units

Provides an introduction to microsystem design. Covers material properties, microfabrication technologies, structural behavior, sensing methods, electromechanical actuation, thermal actuation and control, multi-domain modeling, noise, and microsystem packaging. Applies microsystem modeling, and manufacturing principles to the design and analysis a variety of microscale sensors and actuators (e.g., optical MEMS, bioMEMS, and inertial sensors). Emphasizes modeling and simulation in the design process. Students taking the graduate version complete additional assignments.

Staff

6.719 Nanoelectronics

Subject meets with 6.701
Prereq: 6.003
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

4-0-8 units

Meets with undergraduate subject 6.701, but requires the completion of additional/different homework assignments and or projects. See subject description under 6.701.

M. A. Baldo

6.720[J] Integrated Microelectronic Devices

Same subject as 3.43[J]
Prereq: 6.012 or 3.42
G (Fall)
4-0-8 units

Covers physics of microelectronic semiconductor devices for integrated circuit applications. Topics include semiconductor fundamentals, p-n junction, metal-oxide semiconductor structure, metal-semiconductor junction, MOS field-effect transistor, and bipolar junction transistor. Studies modern nanoscale devices, including electrostatic scaling, materials beyond Si, carrier transport from the diffusive to the ballistic regime. Emphasizes physical understanding of device operation through energy band diagrams and short-channel MOSFET device design. Includes device modeling exercises. Familiarity with MATLAB required. 2 Engineering Design Points.

D. A. Antoniadis, J. A. del Alamo, H. L. Tuller

6.728 Applied Quantum and Statistical Physics

Prereq: 6.003, 18.06
G (Fall)
4-0-8 units

Elementary quantum mechanics and statistical physics. Introduces applied quantum physics. Emphasizes experimental basis for quantum mechanics. Applies Schrodinger's equation to the free particle, tunneling, the harmonic oscillator, and hydrogen atom. Variational methods. Elementary statistical physics; Fermi-Dirac, Bose-Einstein, and Boltzmann distribution functions. Simple models for metals, semiconductors, and devices such as electron microscopes, scanning tunneling microscope, thermonic emitters, atomic force microscope, and more.

P. L. Hagelstein

6.730 Physics for Solid-State Applications

Prereq: 6.013, 6.728
G (Spring)
5-0-7 units

Classical and quantum models of electrons and lattice vibrations in solids, emphasizing physical models for elastic properties, electronic transport, and heat capacity. Crystal lattices, electronic energy band structures, phonon dispersion relations, effective mass theorem, semiclassical equations of motion, electron scattering and semiconductor optical properties. Band structure and transport properties of selected semiconductors. Connection of quantum theory of solids with quasi-Fermi levels and Boltzmann transport used in device modeling.

Q. Hu, R. Ram

6.731 Semiconductor Optoelectronics: Theory and Design

Prereq: 6.728, 6.012
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

3-0-9 units

Focuses on the physics of the interaction of photons with semiconductor materials. Uses the band theory of solids to calculate the absorption and gain of semiconductor media; and uses rate equation formalism to develop the concepts of laser threshold, population inversion, and modulation response. Presents theory and design for photodetectors, solar cells, modulators, amplifiers, and lasers. Introduces noise models for semiconductor devices, and applications of optoelectronic devices to fiber optic communications.

R. J. Ram

6.732 Physics of Solids

Prereq: 6.730 or 8.231
G (Fall)
4-0-8 units

Continuation of 6.730 emphasizing applications-related physical issues in solids. Topics include: electronic structure and energy band diagrams of semiconductors, metals, and insulators; Fermi surfaces; dynamics of electrons under electric and magnetic fields; classical diffusive transport phenomena such as electrical and thermal conduction and thermoelectric phenomena; quantum transport in tunneling and ballistic devices; optical properties of metals, semiconductors, and insulators; impurities and excitons; photon-lattice interactions; Kramers-Kronig relations; optoelectronic devices based on interband and intersubband transitions; magnetic properties of solids; exchange energy and magnetic ordering; magneto-oscillatory phenomena; quantum Hall effect; superconducting phenomena and simple models.

Q. Hu

6.735, 6.736 Advanced Topics in Materials, Devices, and Nanotechnology

Prereq: Permission of instructor
G (Fall, Spring)
Not offered regularly; consult department

3-0-9 units
Can be repeated for credit.

Advanced study of topics in materials, devices, and nanotechnology. Specific focus varies from year to year.

Consult Department

6.774 Physics of Microfabrication: Front End Processing

Prereq: 6.152[J]
G (Fall)
Not offered regularly; consult department

3-0-9 units

Presents advanced physical models and practical aspects of front-end microfabrication processes, such as oxidation, diffusion, ion implantation, chemical vapor deposition, atomic layer deposition, etching, and epitaxy. Covers topics relevant to CMOS, bipolar, and optoelectronic device fabrication, including high k gate dielectrics, gate etching, implant-damage enhanced diffusion, advanced metrology, stress effects on oxidation, non-planar and nanowire device fabrication, SiGe and fabrication of process-induced strained Si. Exposure to CMOS process integration concepts, and impacts of processing on device characteristics. Students use modern process simulation tools.

J. L. Hoyt, L. R. Reif

6.775 CMOS Analog and Mixed-Signal Circuit Design

Prereq: 6.301
G (Spring)
3-0-9 units

A detailed exposition of the principles involved in designing and optimizing analog and mixed-signal circuits in CMOS technologies. Small-signal and large-signal models. Systemic methodology for device sizing and biasing. Basic circuit building blocks. Operational amplifier design. Large signal considerations. Principles of switched capacitor networks including switched-capacitor and continuous-time integrated filters. Basic and advanced A/D and D/A converters, delta-sigma modulators, RF and other signal processing circuits. Design projects on op amps and subsystems are a required part of the subject. 4 Engineering Design Points.

H. S. Lee

6.776 High Speed Communication Circuits

Prereq: 6.301
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

3-3-6 units

Principles and techniques of high-speed integrated circuits used in wireless/wireline data links and remote sensing. On-chip passive component design of inductors, capacitors, and antennas. Analysis of distributed effects, such as transmission line modeling, S-parameters, and Smith chart. Transceiver architectures and circuit blocks, which include low-noise amplifiers, mixers, voltage-controlled oscillators, power amplifiers, and frequency dividers. Involves IC/EM simulation and laboratory projects.

R. Han

6.777[J] Design and Fabrication of Microelectromechanical Systems

Same subject as 2.372[J]
Subject meets with 2.374[J], 6.717[J]

Prereq: 6.003 or 2.003[J], Physics II (GIR); or permission of instructor
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

3-0-9 units

Provides an introduction to microsystem design. Covers material properties, microfabrication technologies, structural behavior, sensing methods, electromechanical actuation, thermal actuation and control, multi-domain modeling, noise, and microsystem packaging. Applies microsystem modeling, and manufacturing principles to the design and analysis a variety of microscale sensors and actuators (e.g., optical MEMS, bioMEMS, and inertial sensors). Emphasizes modeling and simulation in the design process. Students taking the graduate version complete additional assignments. 4 Engineering Design Points.

Staff

6.780[J] Control of Manufacturing Processes

Same subject as 2.830[J]
Prereq: 2.008, 6.041B, 6.152[J], or 15.064[J]
G (Spring)
3-0-9 units

See description under subject 2.830[J].

D. E. Hardt, D. S. Boning

6.781[J] Nanostructure Fabrication

Same subject as 2.391[J]
Prereq: 6.152[J], 6.161, or 2.710; or permission of instructor
G (Spring)
4-0-8 units

Describes current techniques used to analyze and fabricate nanometer-length-scale structures and devices. Emphasizes imaging and patterning of nanostructures, including fundamentals of optical, electron (scanning, transmission, and tunneling), and atomic-force microscopy; optical, electron, ion, and nanoimprint lithography, templated self-assembly, and resist technology. Surveys substrate characterization and preparation, facilities, and metrology requirements for nanolithography. Addresses nanodevice processing methods, such as liquid and plasma etching, lift-off, electroplating, and ion-implant. Discusses applications in nanoelectronics, nanomaterials, and nanophotonics.

K. K. Berggren

Computer Science

6.801 Machine Vision

Subject meets with 6.866
Prereq: 6.003 or permission of instructor
Acad Year 2016-2017: U (Fall)
Acad Year 2017-2018: Not offered

3-0-9 units

Deriving a symbolic description of the environment from an image. Understanding physics of image formation. Image analysis as an inversion problem. Binary image processing and filtering of images as preprocessing steps. Recovering shape, lightness, orientation, and motion. Using constraints to reduce the ambiguity. Photometric stereo and extended Gaussian sphere. Applications to robotics; intelligent interaction of machines with their environment. Students taking the graduate version complete different assignments.

B. K. P. Horn

6.802[J] Foundations of Computational and Systems Biology

Same subject as 20.390[J]
Subject meets with 6.874[J], 20.490, HST.506[J]

Prereq: Biology (GIR), 6.0002 or 6.01; 7.05; or permission of instructor
U (Spring)
3-0-9 units

Provides an introduction to computational and systems biology. Includes units on the analysis of protein and nucleic acid sequences, protein structures, and biological networks. Presents principles and methods used for sequence alignment, motif finding, expression array analysis, structural modeling, structure design and prediction, and network analysis and modeling. Techniques include dynamic programming, Markov and hidden Markov models, Bayesian networks, clustering methods, and energy minimization approaches. Exposes students to emerging research areas. Designed for students with strong backgrounds in either molecular biology or computer science. Some foundational material covering basic programming skills, probability and statistics is provided for students with less quantitative backgrounds. Students taking graduate version complete additional assignments.

D. K. Gifford, T. S. Jaakkola

6.803 The Human Intelligence Enterprise

Subject meets with 6.833
Prereq: 6.034 or permission of instructor
U (Spring)
3-0-9 units

Analyzes seminal work directed at the development of a computational understanding of human intelligence, such as work on learning, language, vision, event representation, commonsense reasoning, self reflection, story understanding, and analogy. Reviews visionary ideas of Turing, Minsky, and other influential thinkers. Examines the implications of work on brain scanning, developmental psychology, and cognitive psychology. Emphasis on discussion and analysis of original papers. Students taking graduate version complete additional assignments. Enrollment limited.

P. H. Winston

6.804[J] Computational Cognitive Science

Same subject as 9.66[J]
Subject meets with 9.660

Prereq: 6.008, 6.036, 6.041B, 9.40, 18.05, or permission of instructor
U (Fall)
3-0-9 units

See description under subject 9.66[J].

J. Tenenbaum

6.805[J] Foundations of Information Policy

Same subject as STS.085[J]
Subject meets with STS.487

Prereq: Permission of instructor
U (Fall)
3-0-9 units. HASS-S

Studies the growth of computer and communications technology and the new legal and ethical challenges that reflect tensions between individual rights and societal needs. Topics include computer crime; intellectual property restrictions on software; encryption, privacy, and national security; academic freedom and free speech. Students meet and question technologists, activists, law enforcement agents, journalists, and legal experts. Instruction and practice in oral and written communication provided. Students taking graduate version complete additional assignments. Enrollment limited.

H. Abelson, M. Fischer, D. Weitzner

6.806 Advanced Natural Language Processing

Subject meets with 6.864
Prereq: 6.046[J] or permission of instructor
U (Fall)
3-0-9 units

Introduces the study of human language from a computational perspective, including syntactic, semantic and discourse processing models. Emphasizes machine learning methods and algorithms. Uses these methods and models in applications such as syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization. Students taking graduate version complete additional assignments.

R. A. Barzilay

6.807 Computational Fabrication

Prereq: 6.837 or permission of instructor
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: U (Spring)

3-0-9 units

Introduces computational aspects of computer-aided design and manufacturing. Explores relevant methods in the context of additive manufacturing (e.g., 3D printing). Topics include computer graphics (geometry modeling, solid modeling, procedural modeling), physically-based simulation (kinematics, finite element method), 3D scanning/geometry processing, and an overview of 3D fabrication methods. Exposes students to the latest research in computational fabrication.

W. Matusik

6.809[J] Interactive Music Systems (New)

Same subject as 21M.385[J]
Subject meets with 21M.585

Prereq: 21M.301, 6.01; or permission of instructor
U (Fall, Spring)
3-0-9 units. HASS-A

See description under subject 21M.385[J]. Limited to 18.

E. Egozy, L. Kaelbling

6.811[J] Principles and Practice of Assistive Technology

Same subject as 2.78[J], HST.420[J]
Prereq: Permission of instructor
U (Fall)
2-4-6 units

Students work closely with people with disabilities to develop assistive and adaptive technologies that help them live more independently. Covers design methods and problem-solving strategies; human factors; human-machine interfaces; community perspectives; social and ethical aspects; and assistive technology for motor, cognitive, perceptual, and age-related impairments. Prior knowledge of one or more of the following areas useful: software; electronics; human-computer interaction; cognitive science; mechanical engineering; control; or MIT hobby shop, MIT PSC, or other relevant independent project experience.

R. C. Miller, J. E. Greenberg, J. J. Leonard

6.813 User Interface Design and Implementation

Subject meets with 6.831
Prereq: 6.005, 6.031, or permission of instructor
U (Spring)
4-0-8 units

Examines human-computer interaction in the context of graphical user interfaces. Covers human capabilities, design principles, prototyping techniques, evaluation techniques, and the implementation of graphical user interfaces. Includes short programming assignments and a semester-long group project. Students taking the graduate version also have readings from current literature and additional assignments. Enrollment limited.

R. C. Miller

6.814 Database Systems

Subject meets with 6.830
Prereq: 6.033; 6.046[J] or 6.006; or permission of instructor
U (Fall)
3-0-9 units

Topics related to the engineering and design of database systems, including data models; database and schema design; schema normalization and integrity constraints; query processing; query optimization and cost estimation; transactions; recovery; concurrency control; isolation and consistency; distributed, parallel and heterogeneous databases; adaptive databases; trigger systems; pub-sub systems; semi structured data and XML querying. Lecture and readings from original research papers. Semester-long project and paper. Students taking graduate version complete different assignments. Enrollment may be limited.

S. R. Madden

6.815 Digital and Computational Photography

Subject meets with 6.865
Prereq: Calculus II (GIR), 6.005 or 6.031
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: U (Fall)

3-0-9 units

Presents fundamentals and applications of hardware and software techniques used in digital and computational photography, with an emphasis on software methods. Provides sufficient background to implement solutions to photographic challenges and opportunities. Topics include cameras and image formation, image processing and image representations, high-dynamic-range imaging, human visual perception and color, single view 3-D model reconstruction, morphing, data-rich photography, super-resolution, and image-based rendering. Students taking graduate version complete additional assignments.

F. P. Durand

6.816 Multicore Programming

Subject meets with 6.836
Prereq: 6.006
U (Spring)
4-0-8 units

Introduces principles and core techniques for programming multicore machines. Topics include locking, scalability, concurrent data structures, multiprocessor scheduling, load balancing, and state-of-the-art synchronization techniques, such as transactional memory. Includes sequence of programming assignments on a large multicore machine, culminating with the design of a highly concurrent application. Students taking graduate version complete additional assignments.

N. Shavit

6.819 Advances in Computer Vision

Subject meets with 6.869
Prereq: 6.041B or 6.042[J]; 18.06
U (Fall)
3-0-9 units

Advanced topics in computer vision with a focus on the use of machine learning techniques and applications in graphics and human-computer interface. Covers image representations, texture models, structure-from-motion algorithms, Bayesian techniques, object and scene recognition, tracking, shape modeling, and image databases. Applications may include face recognition, multimodal interaction, interactive systems, cinematic special effects, and photorealistic rendering. Covers topics complementary to 6.801. Students taking graduate version complete additional assignments.

W. T. Freeman, A. Torralba

6.820 Foundations of Program Analysis

Prereq: 6.035
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-0-9 units

Presents major principles and techniques for program analysis. Includes formal semantics, type systems and type-based program analysis, abstract interpretation and model checking and synthesis. Emphasis on Haskell and Ocaml, but no prior experience in these languages is assumed. Student assignments include implementing of techniques covered in class, including building simple verifiers.

A. Solar-Lezama

6.823 Computer System Architecture

Prereq: 6.004
G (Spring)
4-0-8 units

Introduction to the principles underlying modern computer architecture. Emphasizes the relationship among technology, hardware organization, and programming systems in the evolution of computer architecture. Topics include pipelined, out-of-order, and speculative execution; caches, virtual memory and exception handling, superscalar, very long instruction word (VLIW), vector, and multithreaded processors; on-chip networks, memory models, synchronization, and cache coherence protocols for multiprocessors.

Arvind, J. S. Emer, D. Sanchez

6.824 Distributed Computer Systems Engineering

Prereq: 6.033, permission of instructor
G (Spring)
3-0-9 units

Abstractions and implementation techniques for engineering distributed systems: remote procedure call, threads and locking, client/server, peer-to-peer, consistency, fault tolerance, and security. Readings from current literature. Individual laboratory assignments culminate in the construction of a fault-tolerant and scalable network file system. Programming experience with C/C++ required. Enrollment limited.

R. T. Morris, M. F. Kaashoek

6.828 Operating System Engineering

Prereq: 6.005 or 6.031, 6.033
G (Fall)
3-6-3 units

Fundamental design and implementation issues in the engineering of operating systems. Lectures based on the study of a symmetric multiprocessor version of UNIX version 6 and research papers. Topics include virtual memory; file system; threads; context switches; kernels; interrupts; system calls; interprocess communication; coordination, and interaction between software and hardware. Individual laboratory assignments accumulate in the construction of a minimal operating system (for an x86-based personal computer) that implements the basic operating system abstractions and a shell. Knowledge of programming in the C language is a prerequisite.

M. F. Kaashoek

6.829 Computer Networks

Prereq: 6.033 or permission of instructor
G (Fall)
4-0-8 units

Topics on the engineering and analysis of network protocols and architecture, including architectural principles for designing heterogeneous networks; transport protocols; Internet routing; router design; congestion control and network resource management; wireless networks; network security; naming; overlay and peer-to-peer networks. Readings from original research papers. Semester-long project and paper.

H. Balakrishnan, D. Katabi

6.830 Database Systems

Subject meets with 6.814
Prereq: 6.033; 6.046[J] or 6.006; or permission of instructor
G (Fall)
3-0-9 units

Topics related to the engineering and design of database systems, including data models; database and schema design; schema normalization and integrity constraints; query processing; query optimization and cost estimation; transactions; recovery; concurrency control; isolation and consistency; distributed, parallel and heterogeneous databases; adaptive databases; trigger systems; pub-sub systems; semi structured data and XML querying. Lecture and readings from original research papers. Semester-long project and paper. Students taking graduate version complete different assignments. Enrollment may be limited.

S. R. Madden

6.831 User Interface Design and Implementation

Subject meets with 6.813
Prereq: 6.005, 6.031, or permission of instructor
G (Spring)
4-0-8 units

Examines human-computer interaction in the context of graphical user interfaces. Covers human capabilities, design principles, prototyping techniques, evaluation techniques, and the implementation of graphical user interfaces. Includes short programming assignments and a semester-long group project. Students taking the graduate version also have readings from current literature and additional assignments. Enrollment limited.

R. C. Miller

6.832 Underactuated Robotics

Prereq: 6.141[J], 2.12, 2.165[J], or permission of instructor
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-0-9 units

Covers nonlinear dynamics and control of underactuated mechanical systems, with an emphasis on computational methods. Topics include nonlinear dynamics of passive robots (walkers, swimmers, flyers), motion planning, robust and optimal control, reinforcement learning/approximate optimal control, and the influence of mechanical design on control. Includes examples from biology and applications to legged locomotion, compliant manipulation, underwater robots, and flying machines.

R. Tedrake

6.833 The Human Intelligence Enterprise

Subject meets with 6.803
Prereq: 6.034
G (Spring)
3-0-9 units

Analyzes seminal work directed at the development of a computational understanding of human intelligence, such as work on learning, language, vision, event representation, commonsense reasoning, self reflection, story understanding, and analogy. Reviews visionary ideas of Turing, Minsky, and other influential thinkers. Examines the implications of work on brain scanning, developmental psychology, and cognitive psychology. Emphasis on discussion and analysis of original papers. Requires the completion of additional exercises and a substantial term project. Enrollment limited.

P. H. Winston

6.834[J] Cognitive Robotics

Same subject as 16.412[J]
Prereq: 6.041B, 6.042[J], or 16.09; 16.413 or 6.034
G (Spring)
3-0-9 units

See description under subject 16.412[J].

B. C. Williams

6.835 Intelligent Multimodal User Interfaces

Prereq: 6.005, 6.031, 6.034, or permission of instructor
Acad Year 2016-2017: G (Spring)
Acad Year 2017-2018: Not offered

3-0-9 units

Implementation and evaluation of intelligent multi-modal user interfaces, taught from a combination of hands-on exercises and papers from the original literature. Topics include basic technologies for handling speech, vision, pen-based interaction, and other modalities, as well as various techniques for combining modalities. Substantial readings and a term project, where students build an interface to illustrate one or more themes of the course.

R. Davis

6.836 Multicore Programming

Subject meets with 6.816
Prereq: 6.006
G (Spring)
4-0-8 units

Introduces principles and core techniques for programming multicore machines. Topics include locking, scalability, concurrent data structures, multiprocessor scheduling, load balancing, and state-of-the-art synchronization techniques, such as transactional memory. Includes sequence of programming assignments on a large multicore machine, culminating with the design of a highly concurrent application. Students taking graduate version complete additional assignments.

N. Shavit

6.837 Computer Graphics

Prereq: Calculus II (GIR), 6.005 or 6.031; or permission of instructor
U (Fall)
3-0-9 units

Introduction to computer graphics algorithms, software and hardware. Topics include ray tracing, the graphics pipeline, transformations, texture mapping, shadows, sampling, global illumination, splines, animation and color.

F. P. Durand, W. Matusik

6.838 Advanced Topics in Computer Graphics

Prereq: 6.837
G (Spring)
3-0-9 units
Can be repeated for credit.

In-depth study of an active research topic in computer graphics. Topics change each term. Readings from the literature, student presentations, short assignments, and a programming project.

W. Matusik

6.839 Advanced Computer Graphics

Prereq: 18.06, 6.005, 6.031, 6.837, or permission of instructor
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

3-0-9 units

A graduate level course investigates computational problems in rendering, animation, and geometric modeling. The course draws on advanced techniques from computational geometry, applied mathematics, statistics, scientific computing and other. Substantial programming experience required.

W. Matusik

6.840[J] Theory of Computation

Same subject as 18.4041[J]
Subject meets with 18.404

Prereq: 18.200 or 18.062[J]
G (Fall)
4-0-8 units

See description under subject 18.4041[J].

M. Sipser

6.841[J] Advanced Complexity Theory

Same subject as 18.405[J]
Prereq: 18.404
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

3-0-9 units

See description under subject 18.405[J].

D. Moshkovitz

6.842 Randomness and Computation

Prereq: 6.046[J], 6.840[J]
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

3-0-9 units

The power and sources of randomness in computation. Connections and applications to computational complexity, computational learning theory, cryptography and combinatorics. Topics include: probabilistic proofs, uniform generation and approximate counting, Fourier analysis of Boolean functions, computational learning theory, expander graphs, pseudorandom generators, derandomization.

R. Rubinfeld

6.845 Quantum Complexity Theory

Prereq: 6.045[J], 6.840[J], 18.435[J]
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-0-9 units

Introduction to quantum computational complexity theory, the study of the fundamental capabilities and limitations of quantum computers. Topics include complexity classes, lower bounds, communication complexity, proofs and advice, and interactive proof systems in the quantum world; classical simulation of quantum circuits. The objective is to bring students to the research frontier.

S. Aaronson

6.846 Parallel Computing

Prereq: 6.004 or permission of instructor
G (Spring)
Not offered regularly; consult department

3-0-9 units

Introduction to parallel and multicore computer architecture and programming. Topics include the design and implementation of multicore processors; networking, video, continuum, particle and graph applications for multicores; communication and synchronization algorithms and mechanisms; locality in parallel computations; computational models, including shared memory, streams, message passing, and data parallel; multicore mechanisms for synchronization, cache coherence, and multithreading. Performance evaluation of multicores; compilation and runtime systems for parallel computing. Substantial project required.

A. Agarwal

6.849 Geometric Folding Algorithms: Linkages, Origami, Polyhedra

Prereq: 6.046[J] or permission of instructor
Acad Year 2016-2017: G (Spring)
Acad Year 2017-2018: Not offered

3-0-9 units

Covers discrete geometry and algorithms underlying the reconfiguration of foldable structures, with applications to robotics, manufacturing, and biology. Linkages made from one-dimensional rods connected by hinges: constructing polynomial curves, characterizing rigidity, characterizing unfoldable versus locked, protein folding. Folding two-dimensional paper (origami): characterizing flat foldability, algorithmic origami design, one-cut magic trick. Unfolding and folding three-dimensional polyhedra: edge unfolding, vertex unfolding, gluings, Alexandrov's Theorem, hinged dissections.

E. D. Demaine

6.850 Geometric Computing

Prereq: 6.046[J]
Acad Year 2016-2017: G (Spring)
Acad Year 2017-2018: Not offered

3-0-9 units

Introduction to the design and analysis of algorithms for geometric problems, in low- and high-dimensional spaces. Algorithms: convex hulls, polygon triangulation, Delaunay triangulation, motion planning, pattern matching. Geometric data structures: point location, Voronoi diagrams, Binary Space Partitions. Geometric problems in higher dimensions: linear programming, closest pair problems. High-dimensional nearest neighbor search and low-distortion embeddings between metric spaces. Geometric algorithms for massive data sets: external memory and streaming algorithms. Geometric optimization.

P. Indyk

6.851 Advanced Data Structures

Prereq: 6.046[J]
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

3-0-9 units

More advanced and powerful data structures for answering several queries on the same data. Such structures are crucial in particular for designing efficient algorithms. Dictionaries; hashing; search trees. Self-adjusting data structures; linear search; splay trees; dynamic optimality. Integer data structures; word RAM. Predecessor problem; van Emde Boas priority queues; y-fast trees; fusion trees. Lower bounds; cell-probe model; round elimination. Dynamic graphs; link-cut trees; dynamic connectivity. Strings; text indexing; suffix arrays; suffix trees. Static data structures; compact arrays; rank and select. Succinct data structures; tree encodings; implicit data structures. External-memory and cache-oblivious data structures; B-trees; buffer trees; tree layout; ordered-file maintenance. Temporal data structures; persistence; retroactivity.

E. D. Demaine

6.852[J] Distributed Algorithms

Same subject as 18.437[J]
Prereq: 6.046[J]
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-0-9 units

Design and analysis of concurrent algorithms, emphasizing those suitable for use in distributed networks. Process synchronization, allocation of computational resources, distributed consensus, distributed graph algorithms, election of a leader in a network, distributed termination, deadlock detection, concurrency control, communication, and clock synchronization. Special consideration given to issues of efficiency and fault tolerance. Formal models and proof methods for distributed computation.

N. A. Lynch

6.853 Topics in Algorithmic Game Theory

Prereq: 6.006 or 6.046[J]
Acad Year 2016-2017: G (Spring)
Acad Year 2017-2018: Not offered

3-0-9 units

Presents research topics at the interface of computer science and game theory, with an emphasis on algorithms and computational complexity. Explores the types of game-theoretic tools that are applicable to computer systems, the loss in system performance due to the conflicts of interest of users and administrators, and the design of systems whose performance is robust with respect to conflicts of interest inside the system. Algorithmic focus is on algorithms for equilibria, the complexity of equilibria and fixed points, algorithmic tools in mechanism design, learning in games, and the price of anarchy.

K. Daskalakis

6.854[J] Advanced Algorithms

Same subject as 18.415[J]
Prereq: 6.041B, 6.042[J], or 18.600; 6.046[J]
G (Fall)
5-0-7 units

First-year graduate subject in algorithms. Emphasizes fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Surveys a variety of computational models and the algorithms for them. Data structures, network flows, linear programming, computational geometry, approximation algorithms, online algorithms, parallel algorithms, external memory, streaming algorithms.

A. Moitra, D. R. Karger

6.856[J] Randomized Algorithms

Same subject as 18.416[J]
Prereq: 6.854[J], 6.041B or 6.042[J]
Acad Year 2016-2017: G (Spring)
Acad Year 2017-2018: Not offered

5-0-7 units

Studies how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Models of randomized computation. Data structures: hash tables, and skip lists. Graph algorithms: minimum spanning trees, shortest paths, and minimum cuts. Geometric algorithms: convex hulls, linear programming in fixed or arbitrary dimension. Approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms.

D. R. Karger

6.857 Network and Computer Security

Prereq: 6.033, 6.042[J]
G (Spring)
4-0-8 units

Emphasis on applied cryptography and may include: basic notion of systems security, crypotographic hash functions, symmetric crypotography (one-time pad, stream ciphers, block ciphers), cryptanalysis, secret-sharing, authentication codes, public-key cryptography (encryption, digital signatures), public-key attacks, web browser security, biometrics, electronic cash, viruses, electronic voting, Assignments include a group final project. Topics may vary year to year.

R. L. Rivest

6.858 Computer Systems Security

Prereq: 6.033, 6.005 or 6.031
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-6-3 units

Design and implementation of secure computer systems. Lectures cover attacks that compromise security as well as techniques for achieving security, based on recent research papers. Topics include operating system security, privilege separation, capabilities, language-based security, cryptographic network protocols, trusted hardware, and security in web applications and mobile phones. Labs involve implementing and compromising a web application that sandboxes arbitrary code, and a group final project.

N. B. Zeldovich

6.860[J] Statistical Learning Theory and Applications (New)

Same subject as 9.520[J]
Prereq: 6.867, 6.041B, 18.06, or permission of instructor
G (Fall)
3-0-9 units

See description under subject 9.520[J].

T. Poggio, L. Rosasco

6.861[J] Aspects of a Computational Theory of Intelligence (New)

Same subject as 9.523[J]
Prereq: Permission of instructor
G (Fall)
3-0-9 units

See description under subject 9.523[J].

T. Poggio, S. Ullman

6.862 Applied Machine Learning (New)

Subject meets with 6.036
Prereq: Permission of instructor
G (Spring)
4-0-8 units

Introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction; formulation of learning problems; representation, over-fitting, generalization; clustering, classification, probabilistic modeling; and methods such as support vector machines, hidden Markov models, and Bayesian networks. Students taking graduate version complete different assignments.

R. Barzilay, T. Jaakkola

6.863[J] Natural Language and the Computer Representation of Knowledge

Same subject as 9.611[J]
Prereq: 6.034
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-3-6 units

Explores the relationship between computer representation of knowledge and the structure of natural language. Emphasizes development of analytical skills necessary to judge the computational implications of grammatical formalisms, and uses concrete examples to illustrate particular computational issues. Efficient parsing algorithms for context-free grammars; Treebank grammars and statistical parsing. Question answering systems. Extensive laboratory work on building natural language processing systems. 8 Engineering Design Points.

R. C. Berwick

6.864 Advanced Natural Language Processing

Subject meets with 6.806
Prereq: 6.046[J] or permission of instructor
G (Fall)
3-0-9 units

Introduces the study of human language from a computational perspective, including syntactic, semantic and discourse processing models. Emphasizes machine learning methods and algorithms. Uses these methods and models in applications such as syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization. Students taking graduate version complete additional assignments.

R. A. Barzilay

6.865 Advanced Computational Photography

Subject meets with 6.815
Prereq: Calculus II (GIR), 6.005 or 6.031
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-0-9 units

Presents fundamentals and applications of hardware and software techniques used in digital and computational photography, with an emphasis on software methods. Provides sufficient background to implement solutions to photographic challenges and opportunities. Topics include cameras and image formation, image processing and image representations, high-dynamic-range imaging, human visual perception and color, single view 3-D model reconstruction, morphing, data-rich photography, super-resolution, and image-based rendering. Students taking graduate version complete additional assignments.

F. P. Durand

6.866 Machine Vision

Subject meets with 6.801
Prereq: 6.003 or permission of instructor
Acad Year 2016-2017: G (Fall)
Acad Year 2017-2018: Not offered

3-0-9 units

Intensive introduction to the process of generating a symbolic description of the environment from an image. Students expected to attend the 6.801 lectures as well as occasional seminar meetings on special topics. Material presented in 6.801 is supplemented by reading from the literature. Students required to implement a project on a topic of their choice from the material covered.

B. K. P. Horn

6.867 Machine Learning

Prereq: 6.041B or 18.600, 18.06
G (Fall)
3-0-9 units

Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, non-parametric Bayesian methods, hidden Markov models, and Bayesian networks. Recommended prerequisite: 6.036.

T. Jaakkola, L. P. Kaelbling

6.868[J] The Society of Mind

Same subject as MAS.731[J]
Prereq: Must have read "The Society of Mind" and "The Emotion Machine"; permission of instructor
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

2-0-10 units

Introduction to a theory that tries to explain how minds are made from collections of simpler processes. Treats such aspects of thinking as vision, language, learning, reasoning, memory, consciousness, ideals, emotions, and personality. Incorporates ideas from psychology, artificial intelligence, and computer science to resolve theoretical issues such as wholes vs. parts, structural vs. functional descriptions, declarative vs. procedural representations, symbolic vs. connectionist models, and logical vs. common-sense theories of learning. Enrollment limited.

M. Minsky

6.869 Advances in Computer Vision

Subject meets with 6.819
Prereq: 6.041B or 6.042[J]; 18.06
G (Fall)
3-0-9 units

Advanced topics in computer vision with a focus on the use of machine learning techniques and applications in graphics and human-computer interface. Covers image representations, texture models, structure-from-motion algorithms, Bayesian techniques, object and scene recognition, tracking, shape modeling, and image databases. Applications may include face recognition, multimodal interaction, interactive systems, cinematic special effects, and photorealistic rendering. Covers topics complementary to 6.866. Students taking graduate version complete additional assignments.

W. T. Freeman, A. Torralba

6.870 Advanced Topics in Computer Vision

Prereq: 6.801, 6.869, or permission of instructor
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-0-9 units
Can be repeated for credit.

Seminar exploring advanced research topics in the field of computer vision; focus varies with lecturer. Typically structured around discussion of assigned research papers and presentations by students. Example research areas explored in this seminar include learning in vision, computational imaging techniques, multimodal human-computer interaction, biomedical imaging, representation and estimation methods used in modern computer vision.

W. T. Freeman, B. K. P. Horn, A. Torralba

6.871 Performance Engineering of Software Systems

Subject meets with 6.172
Prereq: 6.004, 6.006, 6.005 or 6.031
G (Fall)
3-12-3 units

Project-based introduction to building efficient, high-performance and scalable software systems. Topics include performance analysis, algorithmic techniques for high performance, instruction-level optimizations, vectorization, cache and memory hierarchy optimization, and parallel programming. Students taking graduate version complete additional assignments.

S. Amarasinghe, C. E. Leiserson

6.872[J] Biomedical Computing

Same subject as HST.950[J]
Prereq: 6.034, 6.036, or permission of instructor
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-0-9 units

Analyzes computational needs of clinical medicine, reviews systems and approaches that have been used to support those needs, and the relationship between clinical data and gene and protein measurements to support precision medicine. Topics include the nature of clinical data, architecture and design of healthcare information systems, privacy and security issues, medical expert systems, predictive models and machine learning from big data in healthcare, and an introduction to bioinformatics. Case studies and guest lectures describe contemporary institutions, systems, and research projects. Term project using large clinical and genomic data sets integrates classroom topics.

G. Alterovitz, P. Szolovits

6.874[J] Computational Systems Biology

Same subject as HST.506[J]
Subject meets with 6.802[J], 20.390[J], 20.490

Prereq: Biology (GIR); 18.600 or 6.041B
G (Spring)
3-0-9 units

Presents advanced machine learning and algorithmic approaches for contemporary problems in biology drawing upon recent advances in the literature. Topics include biological discovery in heterogeneous cellular populations; single cell data analysis; regulatory factor binding; motif discovery; gene expression analysis; regulatory networks (discovery, validation, data integration, protein-protein interactions, signaling, chromatin accessibility analysis); predicting phenotype from genotype; and experimental design (model validation, interpretation of interventions). Computational methods presented include deep learning, dimensionality reduction, clustering, directed and undirected graphical models, significance testing, Dirichlet processes, and topic models. Multidisciplinary team-oriented final research project.

D. K. Gifford

6.875[J] Cryptography and Cryptanalysis

Same subject as 18.425[J]
Prereq: 6.046[J]
G (Spring)
3-0-9 units

A rigorous introduction to modern cryptography. Emphasis on the fundamental cryptographic primitives of public-key encryption, digital signatures, pseudo-random number generation, and basic protocols and their computational complexity requirements.

S. Goldwasser, S. Micali

6.876 Advanced Topics in Cryptography

Prereq: 6.875[J]
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Fall)

3-0-9 units
Can be repeated for credit.

Recent results in cryptography, interactive proofs, and cryptographic game theory.

S. Goldwasser, S. Micali

6.878[J] Advanced Computational Biology: Genomes, Networks, Evolution

Same subject as HST.507[J]
Subject meets with 6.047

Prereq: 6.006, 6.041B, Biology (GIR); or permission of instructor
G (Fall)
4-0-8 units

See description for 6.047. Additionally examines recent publications in the areas covered, with research-style assignments. A more substantial final project is expected, which can lead to a thesis and publication.

M. Kellis

6.881, 6.882 Advanced Topics in Artificial Intelligence

Prereq: Permission of instructor
G (Fall, Spring)
Not offered regularly; consult department

3-0-9 units
Can be repeated for credit.

6.883 Advanced Topics in Artificial Intelligence

Prereq: Permission of instructor
G (Fall, Spring)
Not offered regularly; consult department

3-0-9 units
Can be repeated for credit.

6.884 Advanced Topics in Artificial Intelligence

Prereq: Permission of instructor
G (Fall)
Not offered regularly; consult department

3-0-9 units
Can be repeated for credit.

Advanced study of topics in artificial intelligence. Specific focus varies from year to year. Consult department for details.

Consult Department

6.885-6.888 Advanced Topics in Computer Systems

Prereq: Permission of instructor
G (Fall, IAP, Spring)
Not offered regularly; consult department

3-0-9 units
Can be repeated for credit.

Advanced study of topics in computer systems. Specific focus varies from year to year. Consult department for details.

Consult Department

6.889-6.893 Advanced Topics in Theoretical Computer Science

Prereq: Permission of instructor
G (Fall, Spring)
Not offered regularly; consult department

3-0-9 units
Can be repeated for credit.

Advanced study of topics in theoretical computer science. Specific focus varies from year to year. Consult department for details.

Consult Department

6.894-6.896 Advanced Topics in Graphics and Human-Computer Interfaces

Prereq: Permission of instructor
G (Fall, Spring)
Not offered regularly; consult department

3-0-9 units
Can be repeated for credit.

Advanced study of topics in graphics and human-computer interfaces. Specific focus varies from year to year. Consult department for details.

Consult Department

6.901[J] Innovation Engineering: Moving Ideas to Impact (New)

Same subject as 15.359[J]
Prereq: None
U (Fall)
3-3-6 units

See description under subject 15.359[J].

V. Bulovic, F. Murray

6.902 Engineering Innovation and Design

Engineering School-Wide Elective Subject.
Offered under: 2.723, 6.902, 16.662

Prereq: None
U (Fall, Spring)
3-0-3 units

Project-based seminar in innovative design thinking develops students' ability to conceive, implement, and evaluate successful projects in any engineering discipline. Lectures focus on the iterative design process and techniques to enhance creative analysis. Students use this process to design and implement robust voice recognition applications using a simple web-based system. They also give presentations and receive feedback to sharpen their communication skills for high emotional and intellectual impact. Guest lectures illustrate multidisciplinary approaches to design thinking.

B. Kotelly

6.903 Patents, Copyrights, and the Law of Intellectual Property

Prereq: None
U (Spring)
3-0-6 units

Intensive introduction to the US law of intellectual property with major emphasis on patents, including the process of patent application and the remedies for patent infringement. Also focuses on copyrights and provides a brief look at trademarks and trade secrets. Presents comparisons of what can and cannot be protected, and what rights the owner does and does not obtain. Highlights issues relating to information technology, biogenetic materials, and business methods. Readings include judicial opinions and statutory material. No listeners.

S. M. Bauer

6.904 Ethics for Engineers

Engineering School-Wide Elective Subject.
Offered under: 1.082, 2.900, 6.904, 10.01, 22.014

Prereq: None
U (Fall, Spring)
2-0-4 units

See description under subject 10.01.

D. Doneson, B. L. Trout

6.905 Large-scale Symbolic Systems

Subject meets with 6.945
Prereq: 6.034 or permission of instructor
U (Spring)
3-0-9 units

Concepts and techniques for the design and implementation of large software systems that can be adapted to uses not anticipated by the designer. Applications include compilers, computer-algebra systems, deductive systems, and some artificial intelligence applications. Covers means for decoupling goals from strategy, mechanisms for implementing additive data-directed invocation, work with partially-specified entities, and how to manage multiple viewpoints. Topics include combinators, generic operations, pattern matching, pattern-directed invocation, rule systems, backtracking, dependencies, indeterminacy, memoization, constraint propagation, and incremental refinement. Students taking graduate version complete additional assignments.

G. J. Sussman

6.906 StartMIT: Workshop for Entrepreneurs and Innovators

Subject meets with 6.936
Prereq: None
U (IAP)
4-0-2 units

Designed for students who are interested in entrepreneurship and want to explore the potential commercialization of their research project. Introduces practices for building a successful company, such as idea creation and validation, defining a value proposition, building a team, marketing, customer traction, and possible funding models. Students taking graduate version complete different assignments.

A. Chandrakasan

6.910 Independent Study in Electrical Engineering and Computer Science

Prereq: Permission of instructor
U (Fall, IAP, Spring, Summer)
Units arranged
Can be repeated for credit.

Opportunity for independent study at the undergraduate level under regular supervision by a faculty member. Projects require prior approval.

Consult Department Undergraduate Office

6.911 Engineering Leadership Lab (ESD.05)

Engineering School-Wide Elective Subject.
Offered under: 6.911, 16.650
Subject meets with 6.913[J], 16.667[J]

Prereq: None. Coreq: 6.912 or permission of instructor
U (Fall, Spring)
0-2-1 units
Can be repeated for credit.

Exposes students to engineering frameworks, models, and cases in an interactive, experience-based environment, and hones leadership skills. Students participate in guided reflection on successes and discover opportunities for improvement in a controlled setting. Activities include design-implement activities, role-playing, simulations, case study analysis, and performance assessment by and of other students. Content throughout the term is frequently student-driven. First-year GEL Program students register for 6.911. Second-year GEL Program students register for 6.913. Preference to first-year students in the Bernard M. Gordon-MIT Engineering Leadership Program.

L. McGonagle, J. Feiler

6.912 Engineering Leadership (ESD.054)

Engineering School-Wide Elective Subject.
Offered under: 6.912, 16.651

Prereq: None. Coreq: 6.911 or permission of instructor
U (Fall, Spring)
1-0-2 units
Can be repeated for credit.

Exposes students to the models and methods of engineering leadership within the contexts of conceiving, designing, implementing and operating products, processes and systems. Introduces models and theories, such as the Four Capabilities Framework and the Capabilities of Effective Engineering Leaders. Discusses the appropriate times and reasons to use particular models to deliver engineering success. Includes guest speakers and team projects that change from term to term. May be repeated for credit once with permission of instructor. Preference to first-year students in the Gordon Engineering Leadership Program.

J. Magarian, J. Schindall, L. McGonagle

6.913 Engineering Leadership Lab (ESD.050)

Engineering School-Wide Elective Subject.
Offered under: 6.913, 16.667
Subject meets with 6.911[J], 16.650[J]

Prereq: 6.911
U (Fall, Spring)
0-2-4 units
Can be repeated for credit.

Exposes students to engineering frameworks, models, and cases in an interactive, experience-based environment, and hones leadership skills. Students participate in guided reflection on successes and discover opportunities for improvement in a controlled setting. Activities include design-implement activities, role-playing, simulations, case study analysis, and performance assessment by and of other students. Content throughout the term is frequently student-driven. First year GEL Program students register for 6.911. Second year GEL Program students register for 6.913. Preference to second-year students in the Bernard M. Gordon-MIT Engineering Leadership Program.

L. McGonagle, J. Feiler

6.914 Project Engineering (ESD.052)

Engineering School-Wide Elective Subject.
Offered under: 6.914, 16.669

Prereq: 6.911 or permission of instructor
U (IAP)
1-2-1 units
Credit cannot also be received for 1.040

Students attend a four-day off-site workshop where an introduction to basic principles, methods, and tools for project management in a realistic context are covered. In teams, students create a plan for a project of their choice; past projects include Debris Removal in Haiti and Food Preparation Robot for Restaurants. Develops skills applicable to the management of complex development projects. Topics include cost-benefit analysis, resource and cost estimation, and project control and delivery. Case studies highlight projects in both hardware/construction and software. Preference to students in the Bernard M. Gordon-MIT Engineering Leadership Program.

O. de Weck

6.915[J] Leading Creative and Innovative Teams (New)

Same subject as 16.671[J]
Prereq: None
U (Spring)
6-0-6 units

Empowers future leaders in technology by developing a foundation of personal and team leadership skills. Grounded in research and theory, focuses on practical leadership skills and how they can be assessed, learned, and applied to group situations in technical and engineering contexts. Focuses on how to foster original and creative thinking in groups, and how groups can successfully move creative ideas toward implementation and value creation. Balances traditional learning methods and more experiential ones, such as role play simulations and project-based learning. Enrollment limited to seating capacity of classroom. Admittance may be controlled by lottery.

D. Nino, J. Schindall

6.920 Practical Work Experience

Prereq: None
U (Fall, IAP, Spring, Summer)
0-1-0 units
Can be repeated for credit.

For Course 6 students participating in curriculum-related off-campus work experiences in electrical engineering or computer science. Before enrolling, students must have an employment offer from a company or organization and must find an EECS supervisor. Upon completion of the work the student must submit a letter from the employer evaluating the work accomplished, a substantive final report from the student, approved by the MIT supervisor. Subject to departmental approval. Consult Department Undergraduate Office for details on procedures and restrictions.

Consult Department Undergraduate Office

6.921 6-A Internship

Prereq: None
U (Summer)
0-12-0 units

Provides academic credit for the first assignment of 6-A undergraduate students at companies affiliated with the department's 6-A internship program. Limited to students participating in the 6-A internship program.

T. Palacios

6.922 Advanced 6-A Internship

Prereq: 6.921
U (Spring, Summer)
0-12-0 units

Provides academic credit for the second assignment of 6-A undergraduate students at companies affiliated with the department's 6-A internship program. Limited to students participating in the 6-A internship program.

T. Palacios

6.928[J] Leading Creative Teams (New)

Same subject as 16.990[J]
Prereq: None
G (Fall, Spring)
3-1-5 units

Prepares students to lead teams charged with developing creative solutions to challenging problems. Grounded in research but practical in focus, covers the development of basic leadership capabilities, such as motivating and influencing others, delegating, managing conflict, and communicating effectively; how to create, launch, develop, and adjourn teams; and how to foster creativity in small groups.

D. Nino, J. Schindall

6.929[J] Energy Technology and Policy: From Principles to Practice

Same subject as 5.00[J], 10.579[J], 22.813[J]
Prereq: None
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

3-0-6 units

Develops analytical skills to lead a successful technology implementation with an integrated approach that combines technical, economical and social perspectives. Considers corporate and government viewpoints as well as international aspects, such as nuclear weapons proliferation and global climate issues. Discusses technologies such as oil and gas, nuclear, solar, and energy efficiency. Limited to 100.

J. Deutch

6.930 Management in Engineering

Engineering School-Wide Elective Subject.
Offered under: 2.96, 6.930, 10.806, 16.653

Prereq: None
U (Fall)
3-1-8 units

See description under subject 2.96. Restricted to juniors and seniors.

H. S. Marcus, J.-H. Chun

6.932[J] Linked Data Ventures

Same subject as 15.377[J]
Prereq: 6.005, 6.033, or permission of instructor
G (Spring)
3-0-9 units

Provides practical experience in the use and development of semantic web technologies. Focuses on gaining practical insight from executives and practitioners who use these technologies in their companies. Working in multidisciplinary teams, students complete a term project to develop a sustainable prototype. Concludes with a professional presentation, judged by a panel of experts, and a technical presentation to faculty.

T. Berners-Lee, L. Kagal, K. Rae, R. Sturdevant

6.933 Entrepreneurship in Engineering: The Founder's Journey

Prereq: None
G (Fall, Spring)
4-0-8 units

Immerses students in the experience of an engineer who founds a start-up company. Examines leadership, innovation, and creativity through the lens of an entrepreneur. Suitable for students interested in transforming an idea into a business or other realization for wide-scale societal impact. Covers critical aspects of validating ideas and assessing personal attributes needed to activate and lead a growing organization. Teams explore the basics of new venture creation and experimentation. Emphasizes personal skills and practical experiences. No listeners.

C. Chase

6.935[J] Financial Market Dynamics and Human Behavior

Same subject as 15.481[J]
Prereq: 15.401, 15.414, or 15.415
Acad Year 2016-2017: Not offered
Acad Year 2017-2018: G (Spring)

4-0-5 units

See description under subject 15.481[J].

A. Lo

6.936 StartMIT: Workshop for Entrepreneurs and Innovators

Subject meets with 6.906
Prereq: None
G (IAP)
4-0-2 units

Designed for students who are interested in entrepreneurship and want to explore the potential commercialization of their research project. Introduces practices for building a successful company, such as idea creation and validation, defining a value proposition, building a team, marketing, customer traction, and possible funding models. Students taking graduate version complete different assignments.

A. Chandrakasan

6.941 Statistics for Research Projects: Statistical Modeling and Experiment Design

Prereq: None
G (IAP)
Not offered regularly; consult department

2-2-2 units

Practical introduction to data analysis, statistical modeling, and experimental design, intended to provide essential skills for conducting research. Covers basic techniques such as hypothesis-testing and regression models for both traditional experiments and newer paradignms such as evaluating simulations. Assignments reinforce techniques through analyzing sample datasets and reading case studies. Students with research projects will be encouraged to share their experiences and project-specific questions.

Staff

6.945 Large-scale Symbolic Systems

Subject meets with 6.905
Prereq: 6.034 or permission of instructor
G (Spring)
3-0-9 units

Concepts and techniques for the design and implementation of large software systems that can be adapted to uses not anticipated by the designer. Applications include compilers, computer-algebra systems, deductive systems, and some artificial intelligence applications. Covers means for decoupling goals from strategy, mechanisms for implementing additive data-directed invocation, work with partially-specified entities, and how to manage multiple viewpoints. Topics include combinators, generic operations, pattern matching, pattern-directed invocation, rule systems, backtracking, dependencies, indeterminacy, memoization, constraint propagation, and incremental refinement. Students taking graduate version complete additional assignments.

G. J. Sussman

6.946[J] Classical Mechanics: A Computational Approach

Same subject as 8.351[J], 12.620[J]
Prereq: Physics I (GIR), 18.03, permission of instructor
G (Fall)
3-3-6 units
Credit cannot also be received for 12.008

See description under subject 12.620[J].

J. Wisdom, G. J. Sussman

6.951 Graduate 6-A Internship

Prereq: 6.921 or 6.922
G (Fall, Spring, Summer)
0-12-0 units

Provides academic credit for a graduate assignment of graduate 6-A students at companies affiliated with the department's 6-A internship program. Limited to graduate students participating in the 6-A internship program.

T. Palacios

6.952 Graduate 6-A Internship

Prereq: 6.951
G (Fall, Spring, Summer)
0-12-0 units

Provides academic credit for graduate students who require an additional term at the company to complete the graduate assignment of the department's 6-A internship program. This academic credit is for registration purposes only and cannot be used toward fulfilling the requirements of any degree program. Limited to graduate students participating in the 6-A internship program.

T. Palacios

6.960 Introductory Research in Electrical Engineering and Computer Science

Prereq: Permission of instructor
G (Fall, Spring, Summer)
Units arranged [P/D/F]
Can be repeated for credit.

Enrollment restricted to first-year graduate students in Electrical Engineering and Computer Science who are doing introductory research leading to an SM, EE, ECS, PhD, or ScD thesis. Opportunity to become involved in graduate research, under guidance of a staff member, on a problem of mutual interest to student and supervisor. Individual programs subject to approval of professor in charge.

L. A. Kolodziejski

6.961 Introduction to Research in Electrical Engineering and Computer Science

Prereq: Permission of instructor
G (Fall, Spring, Summer)
3-0-0 units

Seminar on topics related to research leading to an SM, EE, ECS, PhD, or ScD thesis. Limited to first-year regular graduate students in EECS with a fellowship or teaching assistantship.

L. A. Kolodziejski

6.962 Independent Study in Electrical Engineering and Computer Science

Prereq: None
G (Fall, IAP, Spring, Summer)
Units arranged
Can be repeated for credit.

Opportunity for independent study under regular supervision by a faculty member. Projects require prior approval.

L. A. Kolodziejksi

6.980 Teaching Electrical Engineering and Computer Science

Prereq: None
G (Fall, Spring)
Units arranged [P/D/F]
Can be repeated for credit.

For qualified students interested in gaining teaching experience. Classroom, tutorial, or laboratory teaching under the supervision of a faculty member. Enrollment limited by availability of suitable teaching assignments.

H. S. Lee, R. C. Miller

6.981 Teaching Electrical Engineering and Computer Science

Prereq: None
G (Fall, Spring)
Units arranged [P/D/F]
Can be repeated for credit.

For Teaching Assistants in Electrical Engineering and Computer Science, in cases where teaching assignment is approved for academic credit by the department.

H. S. Lee, R. C. Miller

6.991 Research in Electrical Engineering and Computer Science

Prereq: None
G (Fall, Spring, Summer)
Units arranged [P/D/F]
Can be repeated for credit.

For EECS MEng students who are Research Assistants in Electrical Engineering and Computer Science, in cases where the assigned research is approved for academic credit by the department. Hours arranged with research supervisor.

Consult Department Undergraduate Office

6.999 Practical Experience in EECS

Prereq: None
G (Fall, Spring)
Units arranged [P/D/F]

For Course 6 students in the SM/PhD track who seek practical off-campus research experiences or internships in electrical engineering or computer science. Before enrolling, students must have a firm employment offer from a company or organization and secure a research supervisor within EECS. Employers required to document the work accomplished. Research proposals subject to departmental approval; consult departmental Graduate Office.

L. A. Kolodziejski

6.EPE UPOP Engineering Practice Experience

Engineering School-Wide Elective Subject.
Offered under: 1.EPE, 2.EPE, 3.EPE, 6.EPE, 10.EPE, 16.EPE, 22.EPE

Prereq: 2.EPW or permission of instructor
U (Fall, Spring)
0-0-1 units

See description under subject 2.EPE.

Staff

6.EPW UPOP Engineering Practice Workshop

Engineering School-Wide Elective Subject.
Offered under: 1.EPW, 2.EPW, 3.EPW, 6.EPW, 10.EPW, 16.EPW, 20.EPW, 22.EPW

Prereq: None
U (Fall, IAP)
1-0-0 units

See description under subject 2.EPW. Enrollment limited.

Staff

6.S897-6.S899 Special Subject in Computer Science

Prereq: Permission of instructor
G (Fall)
Units arranged
Can be repeated for credit.

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.

Consult Department

6.S911-6.S919 Special Subject in Electrical Engineering and Computer Science

Prereq: Permission of instructor
U (Fall, IAP, Spring)
Not offered regularly; consult department

Units arranged [P/D/F]
Can be repeated for credit.

Covers subject matter not offered in the regular curriculum.

Consult Department

6.S963-6.S967 Special Studies: EECS

Prereq: None
G (Fall)
Units arranged
Can be repeated for credit.

Opportunity for study of graduate-level topics related to electrical engineering and computer science but not included elsewhere in the curriculum. Registration under this subject normally used for situations involving small study groups. Normal registration is for 12 units. Registration subject to approval of professor in charge. Consult the department for details.

L. A. Kolodziejski

6.S974 Special Subject in Electrical Engineering and Computer Science

Prereq: None
G (Fall, Spring)
Not offered regularly; consult department

Units arranged
Can be repeated for credit.

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.

Consult Department

6.S975-6.S979 Special Subject in Electrical Engineering and Computer Science

Prereq: Permission of instructor
G (Fall, Spring)
Not offered regularly; consult department

Units arranged
Can be repeated for credit.

Covers subject matter not offered in the regular curriculum. Consult department to learn of offerings for a particular term.

Consult Department

6.THG Graduate Thesis

Prereq: Permission of instructor
G (Fall, IAP, Spring, Summer)
Units arranged
Can be repeated for credit.

Program of research leading to the writing of an SM, EE, ECS, PhD, or ScD thesis; to be arranged by the student and an appropriate MIT faculty member.

L. A. Kolodziejski

6.THM Master of Engineering Program Thesis

Prereq: 6.UAT
G (Fall, IAP, Spring, Summer)
Units arranged
Can be repeated for credit.

Program of research leading to the writing of an MEng thesis; to be arranged by the student and an appropriate MIT faculty member. Restricted to MEng students who have been admitted to the MEng program.

Consult Department Undergraduate Office

6.UR Undergraduate Research in Electrical Engineering and Computer Science

Prereq: None
U (Fall, IAP, Spring, Summer)
Units arranged [P/D/F]
Can be repeated for credit.

Individual research project arranged with appropriate faculty member or approved supervisor. Forms and instructions for the proposal and final report are available in the EECS Undergraduate Office.

Consult Department Undergraduate Office