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6.008 Introduction to Inference

Prereq: Calculus II (GIR) or permission of instructor
U (Fall)
12 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. Computational laboratory component explores the concepts introduced in class in the context contemporary applications. Students design inference algorithms, investigate their behavior on real data, and discuss experimental results.

P. Golland, G. W. Wornell

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Subjects

http://catalog.mit.edu/subjects/

...series of prerequisites, for example: Prereq: 6.008 or 6.046[J] ; 18.06 Implicit...

Subjects

http://catalog.mit.edu/summer/subjects/

...series of prerequisites, for example: Prereq: 6.008 or 6.046[J] ; 18.06 Implicit...

Electrical Engineering and Computer Science (Course 6-2)

http://catalog.mit.edu/degree-charts/electrical-engineering-computer-science-course-6-2/

Degree Chart for Bachelor of Science in Electrical Engineering and Computer Science (Course 6-2)