Brains, Minds and Machines: example of a graduate curriculum

Education and research are mutually enabling. A healthy educational program stimulates research, and an exciting research program enables new educational tracks. Students will be attracted to the Brains, Minds and Machines program, because:

The subjects below include traditional subjects plus required interdisciplinary subjects.

Computer Science and Engineering

The subjects in the core of the EE&CS curriculum contain material that is essential for just about anyone who is supposed to be a technically trained person. Two of these subjects develop familiarity with the basics of computer programming and hardware design. The computing subject is essential both because it supplies a language for talking about computing and because it develops skill in programming. The hardware subject is essential because it provides a context for looking at nature's hardware.

Students need to develop a sound understanding of the modeling process and the standard techniques for producing models. An understanding of algorithms provides guidance in the design of experimental systems. An understanding of information theory provides ingredients for theories about how brain mechanisms solve communications problems.

Signal Processing (6.341)
Probabilistic Inference (6.041)
Modeling Electrical Circuits (6.301, 6.302)
Modeling Linear Systems Algorithms (6.337J)
Information Theory (18.310)

Applied Mathematics

Beyond the basics of calculus and discrete mathematics, there are mathematics subjects that every veteran of the program must master. Candidates favored at the moment include:

Probability (18.440)
Dynamical Systems (18.385J)
Linear Algebra (18.700)
Functional Analysis (18.102)
Statistical Learning Theory and Applications (9.520, Poggio and Rosasco)

Neuroscience and Cognitive Science

Subjects which provide training in methods and topics in the areas of cognition and neuroscience would include:

Introduction to Cognitive Science (9.012, Gibson, Sinha, and Tenenbaum)
Natural Language Processing and Computation (9.59J, 9.611J)
Infant and Early Childhood Cognition (9.85, Schulz)
Systems Neuroscience (9.011J, Wilson)

Intelligent Machines

To build practical systems, students need to understand traditional problem solving and learning paradigms. To understand the computational basis for intelligence, they need to understand the contributions of at least the vision, language, and motor faculties. Such subjects are at the center of the information part of the program:

Introduction to AI (6.034, Winston)
Visual Object Recognition (Kreiman and Poggio)
Collective Intelligence (Hirsh)
Great Papers (6.833, Winston)

Interdisciplinary Subjects

What is Intelligence (9.S912, Poggio and Ullman)
Computational Cognition (9.660, Tenenbaum and Goodman)
Computational Neuroscience (9.29J, Fee, Wilson, Sompolinsky)
A Computational Approach to Biological Learning (Ullman Poggio)
Introduction to Computation for Brain and Cognitive Sciences" (9.40, Fee and Seung)