Computational Cognitive Science

Calendar

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