Interdisciplinary Doctoral Program in Statistics
Common Core
All students in the Interdisciplinary Doctoral Program in Statistics are required to complete the common core for a total of 27 units.
6.436[J] | Fundamentals of Probability | 12 |
or 18.675 | Theory of Probability |
IDS.190 | Doctoral Seminar in Statistics and Data Science | 3 |
1 | 12 |
| Fundamentals of Statistics | |
| Mathematical Statistics | |
| Mathematical Statistics: a Non-Asymptotic Approach | |
Total Units | 27 |
Program-specific Requirements
Each student must complete the requirements specified by their home department in the lists below by taking one subject from the Computation and Statistics category and one subject from the Data Analysis category.
Aeronautics and Astronautics
| 12 |
| Algorithms for Inference | |
| Machine Learning | |
| Statistical Learning Theory and Applications | |
| Statistics for Engineers and Scientists | |
| Numerical Methods for Stochastic Modeling and Inference | |
| 12 |
| Statistical Communication and Localization Theory | |
| Statistical Methods in Experimental Design | |
| Statistics, Computation and Applications | |
Total Units | 24 |
Brain and Cognitive Sciences
| 12 |
| Biomedical Signal and Image Processing | |
| Machine Learning | |
| Computational Psycholinguistics | |
| Statistical Learning Theory and Applications | |
| Computational Cognitive Science | |
| 12 |
| Statistics for Neuroscience Research | |
| Topics in Neural Signal Processing | |
| Functional Magnetic Resonance Imaging: Data Acquisition and Analysis | |
Total Units | 24 |
Economics
1 | 12 |
| Statistical Learning Theory and Applications | |
| Machine Learning | |
14.192 | Advanced Research and Communication | 12 |
14.386 | New Econometric Methods | 12 |
or 14.387 | Applied Econometrics |
Total Units | 36 |
Mathematics
1 | 12 |
| Nonlinear Optimization | |
| Algebraic Techniques and Semidefinite Optimization | |
| Algorithms for Inference | |
| Machine Learning | |
| Statistical Learning Theory and Applications | |
| Parallel Computing and Scientific Machine Learning | |
| Eigenvalues of Random Matrices | |
| Advanced Algorithms | |
| Randomized Algorithms | |
| Topics in Statistics | |
| 12 |
| Biomedical Signal and Image Processing | |
| Advances in Computer Vision | |
| Statistics for Neuroscience Research | |
| Topics in Neural Signal Processing | |
| Waves and Imaging | |
| Statistics, Computation and Applications | |
Total Units | 24 |
Mechanical Engineering
2.168 | Learning Machines | 12 |
or 6.860[J] | Statistical Learning Theory and Applications |
2.122 | Stochastic Systems | 12 |
or 2.29 | Numerical Fluid Mechanics |
Total Units | 24 |
Political Science
| 12 |
| Machine Learning | |
| Statistical Learning Theory and Applications | |
| Applied Econometrics | |
| 12 |
| Quantitative Research Methods II: Causal Inference | |
| Quantitative Research Methods III: Generalized Linear Models and Extensions | |
| Quantitative Research Methods IV: Advanced Topics | |
Total Units | 24 |
Social and Engineering Systems
| 12 |
| Statistics for Engineers and Scientists | |
| Algorithms for Inference | |
| Machine Learning | |
| Statistical Learning Theory and Applications | |
| Applied Econometrics | |
| Econometrics | |
| Statistical Machine Learning and Data Science | |
| Quantitative Research Methods II: Causal Inference | |
| Quantitative Research Methods III: Generalized Linear Models and Extensions | |
| Quantitative Research Methods IV: Advanced Topics | |
| 12-15 |
| Biomedical Signal and Image Processing | |
| Advances in Computer Vision | |
| Statistics for Neuroscience Research | |
| Topics in Neural Signal Processing | |
| New Econometric Methods and Advanced Research and Communication | |
| Applied Econometrics and Advanced Research and Communication | |
| Waves and Imaging | |
| Statistics, Computation and Applications | |
Total Units | 24-27 |