9 660 - Computational Cognitive Science

Institution:

Massachusetts Institute of Technology

Subject:

Description:

Introduction to computational theories of human cognition. Focuses on principles of inductive learning and inference, and the representation of knowledge. Computational frameworks include Bayesian and hierarchical Bayesian models, probabilistic graphical models, nonparametric statistical models and the Bayesian Occam's razor, sampling algorithms for approximate learning and inference, and probabilistic models defined over structured representations such as first-order logic, grammars, or relational schemas. Applications to understanding core aspects of cognition, such as concept learning and categorization, causal reasoning, theory formation, language acquisition, and social inference. Graduate students complete a final project.

Credits:

3.00

Credit Hours:

Prerequisites:

Prereq: Permission of instructor

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

web.mit.edu/student/

Phone Number:

(617) 253-1000

Regional Accreditation:

New England Association of Schools and Colleges

Calendar System:

Four-one-four plan

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