9.66J | Fall 2004 | Undergraduate
Computational Cognitive Science

Calendar

Lec # Topics key dates
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