Winter 2004
Department of Computing Science
University of Alberta
Instructor: Dale Schuurmans, Ath409, x2-4806, dale@cs.ualberta.ca
| Lecture 1 | Introduction | Tues Jan 6 | ||
| Part 1 | Learning to predict | |||
|---|---|---|---|---|
| Lecture 2 | Linear prediction | Thur Jan 8 | P0 out | |
| Lecture 3 | Generalized linear prediction | Tues Jan 13 | A1 out | |
| Lecture 4 | Neural networks | Thur Jan 15 | ||
| Lecture 5 | Regularization | Tues Jan 20 | ||
| Lecture 6 | Learning theory: bias-variance | Thur Jan 22 | ||
| Lecture 7 | Automated complexity control | Tues Jan 27 | A1 due | |
| Part 2 | Learning to classify | |||
| Lecture 8 | Linear classification, Support Vector Machines | Thur Jan 29 | A2 out | |
| Lecture 9 | Duality | Tues Feb 3 | ||
| Lecture 10 | Kernel methods | Thur Feb 5 | ||
| Lecture 11 | Discrete classification, Computational complexity | Tues Feb 10 | P0 due | |
| Lecture 12 | Decision trees, Noise | Thur Feb 12 | A2 due, A3 out | Reading Week |
| Lecture 13 | Learning theory: Uniform convergence | Tues Feb 24 | ||
| Lecture 14 | Vapnik-Chervonenkis dimension | Thur Feb 26 | ||
| Part 3 | Probability models | |||
| Lecture 15 | Probability models | Tues Mar 2 | ||
| Lecture 16 | Bayesian networks | Thur Mar 4 | A3 due | |
| Lecture 17 | Maximum likelihood estimation | Tues Mar 9 | A4 out | |
| Lecture 18 | Expectation-maximization algorithm | Thur Mar 11 | ||
| Lecture 19 | Bayesian learning | Tues Mar 16 | ||
| Lecture 20 | Bayesian learning (cont.) | Thur Mar 18 | ||
| Part 4 | Ensemble learning methods | |||
| Lecture 21 | Ensemble learning methods | Tues Mar 23 | A4 due | |
| Lecture 22 | Boosting | Thur Mar 25 | ||
| Part 5 | Other learning problems | |||
| Lecture 23 | Unsupervised learning | Tues Mar 30 | ||
| Lecture 24 | Manifold learning | Thur Apr 1 | ||
| Lecture 25 | Reinforcement learning | Tues Apr 6 | Project due |