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Computational Foundations of Cognitive Science 1

Lectures

For the time and place of lectures, check here.

No.	Week	Lecturer	Topic	Reading
1	1	Osborne	Introduction	-
2	1	Osborne	Vectors, vector operations	Anton ch. 1.1
3	1	Osborne	Distances, norm, dot product	Anton ch. 1.2
4	2	Osborne	Matlab overview	McMahon ch. 1
5	2	Osborne	Matlab programming	McMahon ch. 4
6	2	Osborne	Matlab vectors	McMahon ch. 2
7	3	Osborne	Models of learning	-
8	3	Keller	Models of semantic processing	-
9	3	Keller	Basic operations on matrices; matrix product	Anton ch. 3.1
10	4	Keller	Algebraic properties of matrices; transpose; inner and outer product	Anton ch. 3.2
11	4	Keller	Matlab matrices	McMahon ch. 2
12	4	Keller	Inverses; matrices with special forms	Anton chs. 3.2, 3.6
13	5	Keller	Determinants and eigenvectors	Anton ch. 4.1, 4.4
14	5	Keller	Matlab inverses, eigenvectors, plotting and graphics	McMahon ch. 3
15	5	Keller	Convolutions and kernels	-
16	6	Keller	Models of object recognition	-
17	6	Keller	Introduction to probability theory, combinatorial methods	Freund chs. 1.1-1.3
18	6	Keller	Sample spaces, events, probabilities	Freund chs. 2.1-2.4
19	7	Keller	Conditional probability, Bayes' theorem; models of reasoning	Freund chs. 2.5-2.8
20	7	Keller	Discrete and continuous random variables; distributions and densities	Freund chs. 3.1-3.4
21	7	Keller	Special distributions and densities	Freund chs. 5.1, 5.2, 5.4, 6.1-6.3, 6.5
22	8	Keller	Models of eyetracking data	-
-	8	-	no lecture	-
23	8	Osborne	Joint, marginal, and conditional distributions	Freund chs. 3.5-3.7
24	9	Osborne	Probabilistic language models	-
25	9	Osborne	Expectation and variance	Freund chs. 4.1, 4.2
26	9	Osborne	Entropy, Kullback-Leibler divergence	Cover chs. 2.1-2.4
27	10	Osborne	Codes	Cover chs. 5.1-5.4
28	10	Osborne	Minimum description length	Cover chs. 7.1-7.4
29	10	Osborne	Revision	-