| 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)
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| 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 |
