• CMPUT 466/566 Machine Learning (Fall 2018) -- Most of the materials will be hosted on [github pages], with any additional information on eClass. The course number has changed from 551 to 566, but it is otherwise the same course.

    The course will cover the basics of machine learning. The focus in lectures will be on the probabilistic and statistical underpinnings of machine learning, and how the key algorithms are derived. The assignments will be more focused on implementing and running the algorithms. There will be three assignments, one mini-project and a final.

    The course topics will include: maximum likelihood, linear regression, classification algorithms (logistic regression, naive Bayes, SVMs), generalized linear models, representation learning (including neural networks) and evaluation (including empirical evaluation and some theoretical sample complexity results).


  • 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. This course will likely not be offered again in 2019.
  • CMPUT 466/551 Machine Learning (Fall 2017) -- The schedule for this course is 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)