PSYC 5564 Computational Models of Cognition (Fall/Spring: 3 )
Introduction to computational theories of human cognition. Focus on principles of inductive learning and inference, and the representation of knowledge. Computational frameworks covered 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. Undergraduate students must have some prior experience with computer programming and have taken at least five courses in psychology and/or computer science (or instructor permission). Graduate students do not have any formal prerequisites but are encouraged to take into account the undergraduate prerequisites when planning their course of study.
Instructor(s): Joshua Hartshorne
Prerequisites: Two courses in computer programming and undergraduate courses in developmental psychology and cognitive psychology. Students who do not have this background should consult with the instructor on how to prepare.
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Last Updated: 23-Mar-18