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.
James H. Elder
0003G Computer Science and Engineering Building
tel: (416) 736-2100 ext. 66475 fax: (416) 736-5857
email: firstname.lastname@example.org 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.
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.