9.66J | Fall 2004 | Undergraduate
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