CMPUT 466/551 - Machine Learning

Winter 2006
Department of Computing Science
University of Alberta

Instructor: Dale Schuurmans, Ath409, x2-4806, dale@cs.ualberta.ca

TAs: Xiang Wan (xiangwan)
Chonghai Wang (chonghai)

Room: ETL E2-001
Time: TR 2:00-3:30
Office hours: TR 3:30-4:15 (or by appointment)

Textbook: The Elements of Statistical Learning, Hastie, Tibshirani, Friedman, 2001, Springer

This course will cover the fundamental principles of machine learning systems. It will introduce the basic methods used in symbolic machine learning, neural networks, pattern recognition, and graphical probability modelling. These techniques are now widely applied in scientific data analysis, data mining, trainable recognition systems, adaptive resource allocators, and adaptive controllers. The emphasis will be on understanding the fundamental principles that permit effective learning in these systems, realizing their inherent limitations, and exploring the latest advanced techniques employed in machine learning.

Prerequisites

There are no formal prerequisites for this course---all that is required is a basic programming capability and a rudimentary knowledge of probability and statistics. It would be advantageous (but not essential) to have some prior exposure to optimization methods, statistics, and a previous course on artificial intelligence.

Format

The course will consist primarily of prepared lectures that cover the fundamental methods and theories of machine learning. Depending on the interests of the class, there may be an opportunity to cover special topics at the end of the course.

Course work

4 Assignments 15% each (Some programming in Matlab, and some theory)
Project 40%