CSC2515 Fall 2003 - Info

*** TUESDAYS 2-4PM (SS1078) ***

Course info sheets (ps)(pdf)

Instructor: Sam Roweis; email csc2515 at cs dot toronto dot edu
Tutor: Kannan Achan; email csc2515 at cs dot toronto dot edu

Please do NOT send Roweis or Achan email about the class directly to their personal accounts.
They are not able to answer class email except to csc2515 at cs dot toronto dot edu.

Lecture Times: Tuesdays 2-4pm
Lecture Location:HA316 (new!)
Final lecture: December 2.

Tutorial Times: some Tuesdays, 4-5pm
Tutorial Location: TBA
First tutorial September 16, last tutorial TBA.
Office hours: Tuesdays 4-5pm when no tutorial or by appointment

Prerequisite: none for DCS/ECE/STATS grads, instructor permission otherwise; Load: 26L, 13T

class notes, original papers (assigned), chapters from course textbook

Marking Scheme
weekly readings worth 13% (honour system), 3 assignments worth 18% each, one project worth 33%

If you are not registered in the class, it is possible for you to audit it (sit in on the lectures), but only if you get the instructor's permission and follow some rules. See the audit page for more info.

Course Description

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.

All the basic algorithms will be implemented in Matlab, but prior knowledge of Matlab is not required.

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