Teaching
Undergraduate Courses
BCS 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.
BCS 268: Computer Models of Mind
Cognitive scientists attempt to make theories of human perception, language, memory, learning, categorization, reasoning, and many other cognitive functions. A good way of making explicit all the details of a theory, of understanding the implications of a theory, and of comparing the theory with other theories is to implement the theory as a computer model. The course will teach students about current theories of human cognition, and their computer implementations. Emphasis will be placed on a class of computer models known as connectionist models or artificial neural networks.
Graduate Courses
BCS 502: Cognition
This is a team-taught course (by several faculty in Brain & Cognitive Sciences) covering the foundations of human cognition. Emphasis is placed on critically analyzing current theories of mental representations and processing. Topics covered include learning, memory, attention, categorization, cognitive development, and reasoning.
BCS 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).