• CMPUT 659: Optimization Principles for Reinforcement Learning (Winter 2018) -- Most of the materials will be hosted on eClass.
    As a graduate seminar, the main focus of the course will be on research projects. There will be no exams, and all marks will be focused on the research project. The research project can be completed in pairs, or individually. A successful outcome will be a paper, that could be submitted to a workshop or conference, with that paper actually formatted appropriately for the venue. My goal in this course is to help you specify a feasibly-sized project, that can be completed within the course. This will help ensure that you are exposed to completing a paper.

    The learning goasl for this course are to introduce you to more advanced optimization principles for reinforcement learning. It is expected that you have taken a reinforcement learning course. The topics covered will include Bellman operators; some discussion of convergence; optimization objectives in reinforcement learning; difficulties in optimizing objectives in reinforcement learning; step-size selection; eligibility traces; and off-policy learning. I will lecture on some background material, there will be some guest lectures and otherwise each student will be responsible for giving a 20 minute overview of a paper in the readings.
  • CMPUT 466/551 Machine Learning (Fall 2017) -- Most of the materials will be hosted on [github pages], with any additional information on eClass.
    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).
  • 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)