Wed Sep 05. Introduction.
Wed Sep 12. Bayes rule.
Wed Sep 19. Inference.
Mon Oct 01. Maximum likelihood (continued).
Mon Oct 08. Thanksgiving Day.
Wed Oct 17. Linear prediction.
Wed Oct 31. Revision.
Fri Nov 02. Midterm.
Mon Nov 05. Naive Bayes classifier.
Wed Nov 07. Naive Bayes classifier.
Mon Nov 12. Remembrance Day.
Mon Nov 19. Neural networks.
Wed Nov 28. Random forests.
- My favourite book for this course is the book of Stuart Russell and Peter Norvig titled artificial intelligence. Chapter 14 covers probabilistic graphical models. Chapter 15 covers HMMs. Chapter 20 talks about maximum likelihood, the EM algorithm, learning the parameters of graphical models and naive Bayes. Chapter 18 teaches decision trees, linear regression, regularization, neural networks and ensemble learning.
- The machine learning book of Hastie, Tibshirani and Friedman is much more advanced, but it is also a great resource and it is free online: The elements of statistical learning.
- For graphical models and Beta-Bernoulli models, I recommend A Tutorial on Learning with Bayesian Networks David Heckerman.
- Kevin Murphy has compiled a nice page about Bayesian learning.
- Wikipedia tutorial on the: SVD
- The following handout should help you with linear algebra.