Two New Machine Learning Courses Offered this Year! CMPUT 397 and CMPUT 296. Both are planned to become official, regularly offered courses for 2021-2022.


  • CMPUT 296 Basics of Machine Learning (Winter 2021) This is a new course, starting with the Basics of Machine Learning. We will actually cover a lot of the same core concepts as the more advanced machine learning course, 466, though with simpler modeling approaches. The goal is to provide the mathematical foundations to continue onto more advanced ML courses. An Intermediate ML course should be taught in Winter 2021, and will continue to be taught in following years. 296 will become the base course for several following ML courses, and is a better choice than 466 if you plan to take ML in more depth.
    [Website]

  • CMPUT 655 Reinforcement Learning I (Fall 2020). This course will introduce RL, at a graduate level. This course is particularly useful to take if you wanto take the graduate course, RL 2, taught by Rich Sutton or RL Theory by Csaba. We will cover the RL Mooc designed for undergrads, but we will do so more quickly, will cover more advanced topics in class and will have a big focus on a research project. See the website for more info about the schedule and structure:
    [Website]

  • CMPUT 397 Reinforcement Learning (Fall 2020). All materials on the website, [github pages].
  • CMPUT 296 Basics of Machine Learning (Winter 2020) -- Materials still hosted on [Website]
  • CMPUT 397 Reinforcement Learning (Fall 2019). This course was previously taught as CMPUT 366, and was introduced as an explicit course on Reinforcement Learning. Materials still hosted on [github pages].
  • CMPUT 466/566 Machine Learning (Fall 2019) -- Materials still hosted on [github pages].
  • CMPUT 659 Fundamentals of Stochastic Approximation Theory (Winter 2019) -- The schedule for this course is hosted on [github pages].
  • CMPUT 466/566 Machine Learning (Fall 2018) -- Materials still hosted on [github pages].
  • CMPUT 659: Optimization Principles for Reinforcement Learning (Winter 2018) -- Students at the University of Alberta can access scribed notes from this course with a description of the course here.
  • CMPUT 466/551 Machine Learning (Fall 2017) -- Materials still hosted on [github pages].
  • CSCI-B 455 Principles of Machine Learning (Spring 2017) -- All materials publicly available on Canvas
  • CSCI-B 555 Machine Learning (Fall 2016) -- All materials publicly available on Canvas
  • CSCI-B 659 Stochastic Optimization for Machine Learning (Spring 2016) -- All materials publicly available on Canvas
  • CSCI-B 555 Machine Learning (Fall 2015) -- All materials publicly available on Canvas
  • CSCI-B 554 Probabilistic Approaches to AI (Spring 2015) -- Syllabus     Oncourse website     Public schedule
  • CMPUT 379 Operating Systems Concepts (Winter 2014) -- Summary schedule All materials on eClass (restricted access)