High-Dimensional Probability and Statistics

Sebastien Roch, Department of Mathematics, UW-Madison

This course provides a rigorous, self-contained introduction to the area of high-dimensional probability and statistics from a non-asymptotic perspective, aimed at graduate students in mathematics, statistics, computer science and engineering. It will include a focus on core methodology and theory (tail bounds, concentration of measure, random matrices) as well as in-depth exploration of particular model classes (sparse linear models, graphical models, community detection). No statistics background will be assumed. Prior exposure to graduate-level probability (e.g., MATH 733 or ECE 730 or STAT 709) is highly recommended.

Course Information

Course number: MATH/STAT/ECE 888 - Topics in Mathematical Data Science
Semesters taught: Fall 2021
Instructor: Sebastien Roch

Archive

Websites from previous semesters:

Lecture Notes

Lecture notes are available here. Relevant code can be found here.

Textbooks

Topics to be covered are taken partly from the following textbooks (but see also the full list of references):