CSC 2515 -- Fall 2015
Lectures: Thursday 2:10-4
Tutorials: Tuesday 3:10-4
Course schedule: The schedule of lectures, readings, and assignments can be found here. Note that these are all subject to change.
Pre-requisites: 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 (Gaussians, Poisson), and also how to calculate derivatives. Knowledge of linear algebra is also expected, and knowledge of mathematics underlying probability models will be useful. For the programming assignments, you should have some background in programming, and it will be very helpful if you know Matlab or Python.Readings: There is no required textbook for this course. There are several recommended books. We will recommend specific chapters from two books: On the course webpage I will post pointers to relevant readings from Pattern Recognition and Machine Learning by Chris Bishop, and from Machine Learning: A Probabilistic Perspective by Kevin Murphy. I will also provide pointers to other online resources.
Course requirements and grading: The format of the class will be lecture, with some discussion. I strongly encourage interaction and questions. There are assigned readings for each lecture that are intended to prepare you to participate in the class discussion for that day.
The grading in the class will be divided up as follows:
Assignments 40% (2 assignments, each worth 20%)
Tests: There will be a test in class on December 3rd, which will be a closed book exam on all material covered in the lectures, tutorials, and assignments.
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.