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Mathematical Topics in Data Science MATH 475 (3)
An exploration of the mathematics of data science. Analysis of the foundations of algorithms currently used in the field. Potential topics to be covered include: machine learning, compressed sensing, clustering, randomized numerical linear algebra, complex networks and random graph models. Students may repeat this course for further credit under a different topic. Prerequisite: MATH 242, MATH 240 or MATH 232 and STAT 270, all with a minimum grade of C-.
| Section | Instructor | Day/Time | Location |
|---|---|---|---|
|
Nick Dexter |
Jan 10 – Apr 11, 2022: Mon, Wed, Fri, 9:30–10:20 a.m.
|
Burnaby |