Tutorials: Fridays (45:30, Hugh Dempster Pavilion 110)
Instructor Office Hours / Help sessions: Tuesdyas (2:303:30, ICICS 193) and Fridays (12:30, ICICS 238)
Instructor: Mark Schmidt.
Teaching Assistants: Jason Hartford, Robbie Rolin, Sharan Vaswani
Synopsis: This is a graduatelevel 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 of CPSC 340 and it is strongly recommended that you take CPSC 340 first before enrolling in CPSC 540. Topics will (roughly) include linear prediction, graphical models, Bayesian methods, deep learning, online/active/causal learning, and reinforcement learning.
Textbook: No textbook covers all of the topics above, but the one with the most extensive coverage 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.
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 or in EECE. 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 and EECE also need to submit the prerequisites form in the first two weeks of class to stay enrolled. In any case, before registering please read the section below.
CPSC 340 vs. CPSC 540: CPSC 340 and CPSC 540 are roughly structured as one fullyear course. CPSC 340 has more focus on data mining methods and applications of machine learning while CPSC 540 puts more focus on researchlevel machine learning methods and theory. It is strongly recommended that you take CPSC 340 first, as it covers the most common and practicallyuseful techniques. Note that this year multivariate calculus has been added as a prerequisite to CPSC 340. This means that CPSC 340 will be more challenging than previous years and will cover some topics that were previously covered in CPSC 540. If CPSC 340 is the more appropriate class for you but if CPSC 340 is full, you should still sign up for the CPSC 340 waiting list (not CPSC 540) as we may expand the class size: taking CPSC 540 because CPSC 340 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, and note that the Coursera machine learning course is not an adequate replacement for CPSC 340. In 540 it will also be assumed that you have taken a proper class in algorithms and complexity (like CPSC 320) and that you have taken a probability class like MATH 302 (STAT 200 is not enough), while prior exposure to scientific computing (like CPSC 302) will also be helpful. Below are the planned topics for both courses:
CPSC 340  CPSC 540 


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 the assignments, 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.
Grading: Assignments 40%, Final 30%, Project 30%.
Piazza for courserelated qustions
Related Courses: Besides CPSC 340, other closelyrelated courses available at UBC include EECE 360, EECE 592, EOSC 510, STAT 305/306/406, STAT 460/461/560/561, STAT 540, and CPSC 532P. There is some discussion of how 340/540 relate to some of the STAT classes written by a former student (Geoff Roeder) here.
Some related courses that have online notes are: