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: Fall
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.
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