CMPUT 466/551 - Machine Learning
Winter 2006
Department of Computing Science
University of Alberta
Instructor: Dale Schuurmans, Ath409, x2-4806, dale@cs.ualberta.ca
Notes
1. Introduction
2. Linear prediction
3. Generalized linear prediction
4. Neural networks
5. Regularization
6. Learning theory: Bias-variance
7. Automated complexity control
8. Linear classification, Support vector machines
9. Duality
10. Kernels
11. Multiclass prediction
12. Structured output prediction
13. Discrete classification, Computational complexity (skipped)
14. Learning theory: Uniform convergence
15. Vapnik-Chervonenkis dimension
16. Probability models
17. Bayesian networks
18. Maximum likelihood estimation
19. Expectation-Maximization algorithm
20. Bayesian learning
21. Ensemble learning methods
22. Unsupervised learning
23. Manifold learning
24. Reinforcement learning