Basic classifiers: nearest neighbor, decision trees, linear separators.
Generalization: large deviation theory, VC bounds, cross validation.
Generative models: multivariate Gaussian, Fisher linear discriminant, naive Bayes, logistic regression
Support vector machines and kernels.
Ensemble methods: boosting, bagging.
Other topics: the PAC model, multiclass methods, regression, active learning, reinforcement learning.
1. You will need access to Matlab.
2. Useful supplementary material (all but the first are on reserve in the S&E library):
Stuart Russell and Peter Norvig, Artificial intelligence: a modern approach (second edition).
Michael Kearns and Umesh Vazirani, An introduction to computational learning theory.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The elements of statistical learning.
Richard Duda, Peter Hart, and David Stork, Pattern classification.
Homeworks should be turned in at the beginning (first five minutes) of class, on the due date. No late homeworks, please.