Resources

Class Notes and other general resources

· Class notes: Kamaljit Chowdhary

· Class notes: Professor John Hughes

· Class notes: Mario Micheli

· Yanchun Wu’s AM261 page

· Two excellent books that cover some of our topics:

1. Pattern Recognition and Machine Learning, Christopher M. Bishop

2. Information Theory, Inference, and Learning Algorithms, David J.C. MacKay

I. Intuition and large systems

· Shannon’s amazing paper

· Dupuis & Wang’s notes on large deviations

· Diaconis and Freedman on exchangeability and “local independence”

II. Some statistical theory

· SVM: a "gentle guide" (lecture)

· SVM: tutorial (article)

· SVM: tutorial (lecture)

· SVM: consistency (Steinwart)

· SVM: consistency (Vert & Vert)

· SVM: article in Statistical Sciences, with discussion

· SVM: ideas about learning the kernel (article)

· Cross-Validation: observations from experiments

III. Bayesian methods

IV. Dependency graphs and computing

· See section II for an introduction and tutorial

· Tutorial on MM and EM

· On some variations on EM

· MRF's for images and consistency of pseudolikelihood