This course covers a variety of methods that enable a machine to learn. We will cover as much of Duda, Hart, & Stork’s ÔPattern RecognitionÕ as time permits. Topics will include Bayesian decision theory, maximum-likelihood estimation, expectation maximization, nearest-neighbor methods, linear discriminants, support vector machines, artificial neural networks, classification and regression trees, ensemble classifiers, clustering, and self-organizing feature maps. There will be weekly problem sets including some programming. There will be a midterm, and a final exam