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