Modeling of Perception and Cognition

Time: TR 10:10 - 11:25
Place: T - UH 205; R - UH 320.

NOTE: the outline of the material has been updated (January 27).

The textbook and a course packet containing all the required reading are available from the campus store.

NOTE: the programming assignments are described here.

THE FINAL PROJECT IS DUE May 19, 4pm, in the instructor's mailbox in Uris Hall.

NEW (2/24): optional extra reading - a book chapter, titled Models of perceptual learning (S. Edelman and N. Intrator, to appear in Perceptual Learning, MIT Press, 2000).

week 1	1/25 1/27	Levels of analysis; kinds of models; Type I and II theories.
week 2	2/1 2/3	Learning as function approximation; multidimensional spaces.
week 3	2/8 2/10	Ill-posed problems and regularization.
week 4	2/15 2/17	Dimensionality reduction. Multilayer Perceptrons. Feb. 17: the LOVE conference (no class)
week 5	2/22 2/24	Representation and similarity. Radial Basis Functions.
week 6	2/29 3/2	Competition and cooperation in distributed codes. Competitive networks; Self-Organizing Maps.
week 7	3/7 3/9	Dynamics of the brain; chaos.
week 8	3/14 3/16	Statistical view of perception. Bayesian models; statistical inference.
spring break	.. ..	...	.	.
week 9	3/28 3/30	The physical symbol system idea. Symbolic computation.
week 10	4/4 4/6	Symbolic vs. subsymbolic computation; CopyCat.
week 11	4/11 4/13	Similarity and rules
week 12	4/18 4/20	Recursion (also self-reference and recursion)
week 13	4/25 4/27	Evolutionary computation.
week 14	5/2 5/4	May 1: the ARVO conference (no class) Summary: What does the brain do? Computational constraints on theories of the brain.		.

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