| 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 |