Topics in Optimization: Mathematical Foundations of Machine Learning

Sep 3, 2025 — Dec 3, 2025

About the course

This course is a bridge into the machine learning literature for graduate students in mathematics. Compared to existing course offerings in our neighbouring departments (mainly https://www.cs.ubc.ca/~dsuth/532D/23w1 (https://www.cs.ubc.ca/~dsuth/532D/23w1)) we will assume that you know somewhat more analysis, but prior coding experience will not be required. Briefly, the learning objectives are:

• understand the different “learning paradigms” considered in ML (supervised learning, unsupervised learning, reinforcement learning, etc.) and their relation with existing statistical theory
• be comfortable with mathematical tools (eg. kernel methods) which appear commonly in the ML literature but are not well known among pure mathematicians
• see some natural connections between ML theory and: optimization/calculus of variations, measure theory, PDE, etc
• gain fluency reading ML papers (which can be less trustworthy than pure math papers)
• start to think about how to bring your area of mathematical expertise to bear on ML problems.

Registration

This course is available for registration under the Western Dean's Agreement. To register, you must obtain the approval of the course instructor and you must complete the Western Dean's agreement form , using the details below. The completed form should be signed by your home institution department and school of graduate studies, then returned to the host institution of the course.

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