Tutorials: Mondays (5-6, Hugh Dempster Pavilion 110, beginning January 7)
Instructor Office Hours: TBA
TA Office Hours: TBA
Instructor: Mark Schmidt.
Teaching Assistants: TBA
Synopsis: This is a graduate-level course on machine learning, a field that focuses on using automated data analysis for tasks like pattern recognition and prediction. The course will move quickly and assumes a strong background in math and computer science as well as previous experience with statistics and/or machine learning. The class is intended as a continuation CPSC 340 (which is also known as 532M for grad students) and it is strongly recommended that you take CPSC 340 or 532M first before enrolling in CPSC 540. Topics will (roughly) include large-scale machine learning, density estimation, probabilistic graphical models, deep learning, and Bayesian statistics.
Registration and Prerequisites: Graduate and undergraduate students from any department are welcome to take the class, provided that they satisfy the prerequisites. However, you can only register automatically if you are enrolled as a graduate student in CPSC, EECE, or STAT. If you are a graduate student from a different department (or are an undergraduate student satisfying these requirements), you can register by following the instructions here and submitting the prerequisites form here. Graduate students in CPSC/EECE/STAT also need to submit the prerequisites form before the add/drop deadline to stay enrolled.
CPSC 340 vs. CPSC 540: CPSC 340 and CPSC 540 are roughly structured as one full-year course. CPSC 340 (and its graduate version 532M) covers more data mining methods and the methods that are most widely-used in applications of machine learning while CPSC 540 covers more research-level machine learning methods and theory. It is strongly recommended that you take CPSC 340 or 532M first, as it covers the most common and practically-useful techniques. If CPSC 340/532M is full, you should still sign up for the CPSC 340/532M waiting list as we may expand the class size: taking CPSC 540 because CPSC 340/532M is full is a terrible idea. In 540 it will be assumed that you are familiar with the material in the current offering of CPSC 340/532M, and note that the Coursera machine learning course is not an adequate replacement for CPSC 340/532M.
CPSC 540 requires a stronger computer science and math background and will require substantially more work (including proofs and implementing methods from scratch). Note that CPSC grad students typically only take 1-3 courses per term compared to 3-6 for undergraduate students: so you should expect the workload to be up to 3 times higher than in typical courses. If you want an introduction to machine learning, do not have a strong computer science and math background, or are mainly interested in applying machine learning in your research, then CPSC 340/532M is the right course to take. You can always decide to take (or audit) CPSC 540 later.
Auditting: Rather than registering as a student, an alternate option is to register as an auditor. This is a good option for students that may be missing some of the prerequisites or that don't have enough time to do all course requirements, but that still want exposure to the material. For graduate students, the form for auditing the course is available here. For undergraduates, you need to fill out the form here and indicate on the course information section that you wish to "audit". I will describe the auditting requirements and sign these forms on the first day of class.
Textbook: There is no textbook for the course, but the textbook with the most extensive coverage of many of the course's topics is Kevin Murphy's Machine Learning: A Probabilistic Perspective (MLAPP). This book can be purchased from Amazon, is on reserve in the CS Reading Room (ICCS 262), and can be accessed through the library here. Optional readings will be given out of this textbook, in addition to other free online resources.
Grading: Assignments 40%, Final 30%, Project 30%.
Related Courses: Besides CPSC 340/532M, other closely-related courses available at UBC include 500-level CPSC courses by Michael Friedlander, Michiel van de Panne, Leonid Sigal, or Frank Wood. From other departments relevant course are EECE 360/592, EOSC 510/550, LIBR 559D/559N, LINF 530F, STAT 305/306/406/460/461, and most 500-level STAT courses. There is some discussion of how 340/540 relates to some of the STAT classes written by a former student (Geoff Roeder) here.
Some related courses that have online notes are: