CSC2515 Fall 2004 - Info
*** TUESDAYS 2-4PM (GB244) ***
Instructor: Sam Roweis; email csc2515 at cs dot toronto dot edu
Please do NOT send Roweis or tutors email about the class
directly to their personal accounts.
Lecture Times: Tuesdays 2-4pm
Tutorial Times: some Tuesdays, 4-5pm
Prerequisite: none for DCS/ECE/STATS grads, instructor permission otherwise; 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.