| 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 |