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Computational Cognitive Science >> Content Detail



Lecture Notes



Lecture Notes

Lec #Topics
1Introduction (PDF)
2Foundations of Inductive Learning (PDF)
3Knowledge Representation: Spaces, Trees, Features (PDF)
4Knowledge Representation: Language and Logic 1 (PDF)
5Knowledge Representation: Language and Logic 2 (PDF)
6Knowledge Representation: Great Debates 1 (PDF)
7Knowledge Representation: Great Debates 2 (PDF)
8Basic Bayesian Inference (PDF)
9Graphical Models and Bayes Nets (PDF)
10Simple Bayesian Learning 1 (PDF)
11Simple Bayesian Learning 2 (PDF)
12Probabilistic Models for Concept Learning and Categorization 1 (PDF)
13Probabilistic Models for Concept Learning and Categorization 2 (PDF)
14Unsupervised and Semi-supervised Learning (PDF)
15Non-parametric Classification: Exemplar Models and Neural Networks 1 (PDF - 1.4 MB)
16Non-parametric Classification: Exemplar Models and Neural Networks 2 (PDF)
17Controlling Complexity and Occam's Razor 1 (PDF)
18Controlling Complexity and Occam's Razor 2 (PDF)
19Intuitive Biology and the Role of Theories (PDF)
20Learning Domain Structures 1 (PDF - 1.3 MB)
21Learning Domain Structures 2 (PDF)
22Causal Learning (PDF)
23Causal Theories 1 (PDF)
24Causal Theories 2 (PDF)
25Project Presentations

 








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