The following slides have been grouped by topic in the order of presentation and are good for about 20 lectures. Thanks are due to Koryn Grant, Murray Davidson, Eric Liu and Bahram Rezai-Peshravi for debugging them and to John Hudson for valuable discussion of some of the material. The HMM Mini-Toolkit for experiments in this course can be found here.

- Revision of Combinatorics
- Revision of Probability
- Bayesian Learning 1
- Markov Networks
- Information Theory
- Introduction to MML/MDL
- Bayesian Learning 2: EM Algorithm
- Decision Trees
- Artificial Neural Nets
- Genetic Algorithms
- Computational Learning Theory