Winter 2006
Department of Computing Science
University of Alberta
Instructor: Dale Schuurmans, Ath409, x2-4806, dale@cs.ualberta.ca
| Lecture 1 | Introduction | Tues Jan 10 | ||
| Part 1 | Learning to predict | |||
|---|---|---|---|---|
| Lecture 2 | Linear prediction | Thur Jan 12 | P0 out | |
| Lecture 3 | Generalized linear prediction | Tues Jan 17 | A1 out | |
| Lecture 4 | Neural networks | Thur Jan 19 | ||
| Lecture 5 | Regularization | Tues Jan 24 | ||
| Lecture 6 | Learning theory: bias-variance | Thur Jan 26 | ||
| Lecture 7 | Automated complexity control | Tues Jan 31 | A1 due | |
| Part 2 | Learning to classify | |||
| Lecture 8 | Linear classification, Support Vector Machines | Thur Feb 2 | A2 out | |
| Lecture 9 | Duality | Tues Feb 7 | ||
| Lecture 10 | Kernel methods | Thur Feb 9 | ||
| Lecture 11 | Discrete classification, Computational complexity | Tues Feb 14 | P0 due | |
| Lecture 12 | Decision trees, Noise | Thur Feb 16 | A2 due, A3 out | Reading Week |
| Lecture 13 | Learning theory: Uniform convergence | Tues Feb 28 | ||
| Lecture 14 | Vapnik-Chervonenkis dimension | Thur Mar 2 | ||
| Part 3 | Probability models | |||
| Lecture 15 | Probability models | Tues Mar 7 | ||
| Lecture 16 | Bayesian networks | Thur Mar 9 | A3 due | |
| Lecture 17 | Maximum likelihood estimation | Tues Mar 14 | ||
| Lecture 18 | Expectation-maximization algorithm | Thur Mar 16 | ||
| Lecture 19 | Bayesian learning | Tues Mar 21 | A4 out | |
| Lecture 20 | Bayesian learning (cont.) | Thur Mar 23 | ||
| Part 4 | Ensemble learning methods | |||
| Lecture 21 | Ensemble learning methods | Tues Mar 28 | ||
| Lecture 22 | Boosting | Thur Mar 30 | ||
| Part 5 | Other learning problems | |||
| Lecture 23 | Unsupervised learning | Tues Apr 4 | A4 due | |
| Lecture 24 | Manifold learning | Thur Apr 6 | ||
| Lecture 25 | Reinforcement learning | Tues Apr 11 | Project due |