Location & Time: Chemistry Building 122  MW 17:30-19:00

Machine learning is the study of algorithms that learn how to perform a task from prior experience. Machine learning algorithms find widespread application in diverse problem areas, including machine perception, natural language processing, search engines, medical diagnosis, bioinformatics, brain-machine interfaces, financial analysis, gaming and robot navigation.  This course will thus provide students with marketable skills and also with a foundation for further, more in-depth study of machine learning topics.

This course introduces the student to machine learning concepts and techniques applied to pattern recognition problems in a diversity of application areas.  The course takes a probabilistic perspective, but also incorporates a number of non-probabilistic techniques. 

Instructor Information:

James H. Elder

0003G Computer Science and Engineering Building
tel: (416) 736-2100  ext. 66475  fax: (416) 736-5857
email: jelder@yorku.ca   website: www.yorku.ca/jelder

Office Hour:  Friday 12:00-13:00

Complete Syllabus & Schedule


I will post lectures on this website, but will use Moodle to post supplementary readings. We will also use Moodle for discussion - I encourage you to make full use of this facility, but please remember that the two graded assignments must be done individually - do not post solutions or partial solutions.


  1. Introduction
  2. Bayesian Decision Theory
  3. Multivariate Normal Distribution
  4. Linear Regression
  5. Linear Classifiers
  6. Mixture Models and EM
  7. Dimensionality Reduction
  8. Kernel Methods
  9. Boosting
  10. Multi-layer Perceptrons

I reserve the right to make changes to the lectures up to the time of the class. Small changes may also be made after class, e.g., to correct errors. I will indicate in each set of slides the date they were last modified: please verify that you have the most recent versions


Please note that datasets for assignments will be posted on the moodle site for the course.