Computational Cognition & Perception Lab
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Undergraduate Course

BCSC 111: Foundations of Cognitive Science

This course introduces the major theories and findings regarding human cognition. Emphasis is placed on mental representations and processing, especially the interactions between bottom-up and top-down processes. The course integrates knowledge of cognition generated from the field of cognitive psychology with findings from artificial intelligence and cognitive neuroscience. Topics covered include visual perception, language acquisition and use, learning, memory, reasoning, and intelligence.

Graduate Courses

BCSC 512: Computational Methods in Cognitive Science

This course covers a range of computational methods used to analyze data and build theories in cognitive science. Emphasis is placed on probabilistic methods such as those based on maximum likelihood estimation theory or Bayesian statistics. Topics covered include dimensionality reduction (e.g., principal component analysis, factor analysis, independent component analysis), clustering (e.g., mixtures of Normal distributions, hierarchical clustering), induction and reasoning (e.g., Bayesian networks), the analysis of time series (e.g., hidden Markov models, Kalman filters), and neural networks (e.g., Hebbian learning, the backpropagation algorithm).

BCSC 532: Probabilistic Theories of Cognitive Processes

This course is a graduate-level seminar intended to teach students about state-of-the-art probabilistic theories of human cognitive processing and their computational implementations. Topics covered include computational models of language, perception, categorization, numerical cognition, and decision making.