CSC2515 Fall 2002 - Info

*** WEDNESDAYS 3-5PM (BAHEN 1210 !!NEW ROOM!!) ***

Course info sheets (ps)(pdf)

Instructor: Sam Roweis; email csc2515@cs.toronto.edu
Tutor: Kannan Achan; email csc2515@cs.toronto.edu

Please do NOT send Roweis or tutor email about the class directly to their personal accounts.
They are not able to answer class email except to cs2515@cs.toronto.edu.

Lecture Times: Wednesdays 3-5pm
Lecture Location:BA1210 (new!)
First lecture Sept. 11, last lecture December 4.
No lectures on these dates: .

Tutorial Times: some Wednesdays, 5-6pm
Tutorial Location: BA1200 (next door to lecture)
First tutorial September 25, last tutorial TBA.
Office hours: Wednesdays 5-6pm when no tutorial

Prerequisite: none; Load: 26L, 13T

Readings
class notes, original papers (assigned), chapters from various books (optional)

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

Auditing
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.

CSC2515 - Machine Learning || www.cs.toronto.edu/~roweis/csc2515/