CSC2515 Fall 2002 - Info
*** WEDNESDAYS 3-5PM (BAHEN 1210 !!NEW ROOM!!) ***
Instructor: Sam Roweis; email email@example.com
Please do NOT send Roweis or tutor email about the class
directly to their personal accounts.
Lecture Times: Wednesdays 3-5pm
Tutorial Times: some Wednesdays, 5-6pm
Prerequisite: none; Load: 26L, 13T
Basic methods for classification, regression, clustering, time series modeling, and novelty detection. These algorithms will include K-nearest neighbours, naive Bayes, decision trees, support vector machines, logistic regression, generalized additive models, K-means, mixtures of Gaussians, hidden markov models, principal components analysis, factor analysis and independent components analysis. Methods of fitting models including stochastic gradient and conjugate gradient methods, the Expectation Maximization algorithm and Markov Chain Monte Carlo. The fundamental problem of overfitting and techniques for dealing with it such as capacity control and model averaging.