Instructor: Yaniv Plan Office: 1219 Math Annex Email: yaniv (at) math (dot) ubc (dot) ca

Lectures: TuTh, 3:30 – 5:00pm, Henn 301.

Office hours: TBD.

Prerequisites: The course will assume knowledge of linear algebra (and functional analysis) as well as a strong probabilistic intuition. For example, I will assume you have familiarity with stochastic processes, norms, singular values, and Lipschitz functions.

Overview: We study the tools and concepts in high-dimensional probability which support the theoretical foundations of compressed sensing; they also apply to many other problems in machine learning and data science.

Detailed course outline: See here. (We probably won’t cover all of those topics.)

Textbook: There is no required textbook. The following references cover some of the material, and they are available online:

R. Vershynin, High-dimensional probability. This book has the most overlap with our course. (Our course begins by following an earlier course of Vershynin’s on high-dimensional probability.)

Earlier version of this course, which contains a series of notes. For the beginning of the course, we will roughly follow the same notes.

Grading: Students will complete a class project (in teams), including a presentation in class. The project may be a literature review or a mini-research problem.

Instructor:Yaniv PlanOffice: 1219 Math Annex

Email: yaniv (at) math (dot) ubc (dot) ca

Lectures:TuTh, 3:30 – 5:00pm, Henn 301.Office hours:TBD.Prerequisites:The course will assume knowledge of linear algebra (and functional analysis) as well as a strong probabilistic intuition. For example, I will assume you have familiarity with stochastic processes, norms, singular values, and Lipschitz functions.Overview:We study the tools and concepts in high-dimensional probability which support the theoretical foundations of compressed sensing; they also apply to many other problems in machine learning and data science.Detailed course outline:See here. (We probably won’t cover all of those topics.)Textbook:There is no required textbook. The following references cover some of the material, and they are available online:Grading:Students will complete a class project (in teams), including a presentation in class. The project may be a literature review or a mini-research problem.