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Graduate Program

BCS 512: Computational Methods in Cognitive Science

Cross-listed: CS 512 (biennial)
Prerequisites: Includes knowledge of calculus. Knowledge of linear algebra and probability theory will also be helpful (though prior knowledge of these areas is not strictly required). In addition, homeworks require students to write computer programs (preferably in Matlab).
Offered: Spring (beginning 2018)

This course focuses on: (a) statistical tools that are useful for revealing structure in experimental data; and (b) representation and learning in statistical systems and the implications of these systems for the study of cognitive processes. Examples of the applications of computational methods from the cognitive neuroscience literature are examined throughout the course. Topics covered include: principal component analysis, multi-dimensional scaling, hierarchical and non-hierarchical clustering, regression, classification, time series modeling via hidden Markov models and Kalman filters, Hebbian learning, competitive learning, maximum likelihood estimation, and Bayesian estimation.