Lectures: Tuesdays 1:10-3:00 (see below for location)
Tutorials: Fridays 11:10-12:00 in BA1200
Instructor: Richard Zemel; email csc2515prof [at sign] cs.toronto.edu
Office Hours: Thursdays 1:00-2:00pm (Pratt 290D) or by appt.
Tutors: Navdeep Jaitly, Kevin Swersky; email csc2515ta [at sign] cs.toronto.edu
We've had a very difficult time securing a room for this class. As a result, we will be changing classroom venues during the course. Please note the following schedule of course meeting locations (the time is still 1-3 pm on Tuesdays):
September 10th: Sandford Fleming 3202
September 17: *Best Institute (CB) 114
September 24th: Sandford Fleming 3202
October 1st, 8th, 15th and 22nd: Best Institute 114
October 29th and onwards: Bahen 2135
*The Best Institute (CB) is located at 112 College Street.
Please do NOT send the instructor or tutors email about the class directly to their personal accounts. It will be deleted. We will only respond to class email if it is sent to csc2515prof or csc2515ta [at sign] cs.toronto.edu
Course schedule: The schedule of lectures and assignments can be found here.
Prerequisites: You should understand basic probability and statistics, and college-level algebra and calculus. For example it is expected that you know about standard probability distributions (such as Gaussians), and also how to calculate derivatives. Knowledge of linear algebra is also expected. For the programming assignments, you should have some background in programming, and it would be helpful if you know Matlab. Some introductory material for Matlab will be available on the course website as well as in the first tutorial.
Load: About 24 hours of lectures; two assignments; one project; plus one final test.
Required Readings: The textbook for the course is Pattern Recognition and Machine Learning, by Chris Bishop. This will be available in the U of T bookstore. Most of the required readings will be parts of the textbook but there will be some additional readings and we will not cover all of the chapters in the textbook.
Project worth 1/3 of final mark
Remainder of final mark split evenly between two assignments and a final test
Computing: Parts of the assignments will be done in Matlab, but prior knowledge of Matlab is not essential. [Computing Information] [Matlab Information]
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.
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