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 |