Computational Cognitive Science

Lecture Notes

Lec #	Topics
1	Introduction (PDF)
2	Foundations of Inductive Learning (PDF)
3	Knowledge Representation: Spaces, Trees, Features (PDF)
4	Knowledge Representation: Language and Logic 1 (PDF)
5	Knowledge Representation: Language and Logic 2 (PDF)
6	Knowledge Representation: Great Debates 1 (PDF)
7	Knowledge Representation: Great Debates 2 (PDF)
8	Basic Bayesian Inference (PDF)
9	Graphical Models and Bayes Nets (PDF)
10	Simple Bayesian Learning 1 (PDF)
11	Simple Bayesian Learning 2 (PDF)
12	Probabilistic Models for Concept Learning and Categorization 1 (PDF)
13	Probabilistic Models for Concept Learning and Categorization 2 (PDF)
14	Unsupervised and Semi-supervised Learning (PDF)
15	Non-parametric Classification: Exemplar Models and Neural Networks 1 (PDF - 1.4 MB)
16	Non-parametric Classification: Exemplar Models and Neural Networks 2 (PDF)
17	Controlling Complexity and Occam’s Razor 1 (PDF)
18	Controlling Complexity and Occam’s Razor 2 (PDF)
19	Intuitive Biology and the Role of Theories (PDF)
20	Learning Domain Structures 1 (PDF - 1.3 MB)
21	Learning Domain Structures 2 (PDF)
22	Causal Learning (PDF)
23	Causal Theories 1 (PDF)
24	Causal Theories 2 (PDF)
25	Project Presentations