Statistical Learning Theory

Fall 2020



Course Information


Video recording of lectures

These are also available in myCourses.



Here are some pointers to possible projects. Your project is not expected to include any original new work. I expect a critical review of some work. By "critical" I mean that you should formulate some reaction to what you are reading and not just summarize. The expected size is 10 pages with a variance of 2 pages. This is still being updated with more modern references and some of the broken links have to be fixed.

Below are some suggestions for topics that could be explored in more detail for the final project, and a couple of examples of relevant papers for each topic. All of these would make good literature synthesis topic, though you should be prepared to find additional relevant papers on your own! (Google Scholar is a good place to start.) This list might also inspire ideas for research projects. Of course you are welcome to choose a topic that is not on the list.

Probabilistic bisimulation and metrics:

Active Learning:

The Multi-Armed Bandit Problem:

Domain Adaptation and/or Multi-Source Learning:

Privacy-Preserving Machine Learning:

Learning Bounds for Reinforcement Learning:

Online Convex Optimization:


PAC-Bayes Bounds:


Geometrical Ideas

GANs and metrics

Academic Integrity

McGill University values academic integrity. Therefore all students must understand the meaning and consequences of cheating, plagiarism and other academic offenses under the Code of Student Conduct and Disciplinary Procedures (see for more information). Most importantly, work submitted for this course must represent your own efforts. Copying assignments or tests from any source, completely or partially, or allowing others to copy your work, will not be tolerated.

Every student has the right to submit written work that is to be graded, in English or in French.

Chaque étudiant a le droit de soumettre en français ou en anglais tout travail écrit.