| 1 |
Introduction |
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| 2 |
Foundations of Inductive Learning |
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| 3 |
Knowledge Representation: Spaces, Trees, Features |
Problem set 1 out |
| 4 |
Knowledge Representation: Language and Logic 1 |
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| 5 |
Knowledge Representation: Language and Logic 2 |
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| 6 |
Knowledge Representation: Great Debates 1 |
Problem set 1 due |
| 7 |
Knowledge Representation: Great Debates 2 |
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| 8 |
Basic Bayesian Inference |
Problem set 2 out |
| 9 |
Graphical Models and Bayes Nets |
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| 10 |
Simple Bayesian Learning 1 |
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| 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 |
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| 15 |
Non-parametric Classification: Exemplar Models and Neural Networks 1 |
Problem set 3 due |
| 16 |
Non-parametric Classification: Exemplar Models and Neural Networks 2 |
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| 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 |
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| 20 |
Learning Domain Structures 1 |
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| 21 |
Learning Domain Structures 2 |
Problem set 4 due |
| 22 |
Causal Learning |
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| 23 |
Causal Theories 1 |
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| 24 |
Causal Theories 2 |
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| 25 |
Project Presentations |
Project due |
