Aukosh Jagannath

Course: STAT 946 — Mathematics of Data Science (Adv. Topics)
Section: Lec 001— Winter 2021
Location/Medium: Live-stream [Medium TBD] +/- DC 1351 (See note below)
Lecture Times: TTh 1-2.20pm EST (GMT-5)
Office Hours: TBD

Syllabus: The goal of this course is to quickly orient you toward mathematical problems at the heart of data science. This course will focus on the statistical and computational limits of high-dimensional problems. The first third of the course will serve as an introduction to the basic tools of the course, namely high-dimensional probability and random matrix theory. We will then cover a broad range of applications. Depending on interest, applications may include:

Suggested Background: This will be an advanced topics course and will be mathematically rigorous. We will focus on theoretical results regarding both statistical and computational limits. It is strongly recommended that the student have successfully completed at least one advanced course in probability, stochastic processes, or mathematical statistics, as well as courses in analysis and linear algebra. Please contact the instructor to confirm if your background is sufficient.

Note: This course is officially listed as occuring in person. However, the university has currently suspended on-campus activities for this course until at least Jan 25th. Instead, I plan to both live-stream and record. Attendance of the live-stream is strongly encouraged, if feasible, but not required.