STAT210B: Theoretical Statistics

Description

Instructor: Song Mei (songmei [at] berkeley.edu)
Lectures: Tuesday/Thursday 09:30-11:00. Lewis 9.
Office Hours: Tu 1:30 - 3 pm. Evans 387.
GSI: Eric Xia (ericzxia [at] berkeley.edu)
Office Hours: W 1-2 pm; F 11 am - 12 pm. Evans 444.
Discussion Sessions: F 10 - 11 am every other week (first session on Jan 28).

This is an advanced graduate course on mathematical statistics, following up on the introductory course STAT 210a. Topics to be covered include tail bounds and basic aspects of concentration of measure, uniform laws of large number, metric entropy and chaining arguments, Gaussian comparison inequalities, covariance estimation and non-asymptotic random matrix theory, sparse high-dimensional models, structured forms of principal component analysis, non-parametric regression, and minimax lower bounds.

Announcements

• First lecture starts on Jan 18, 2022 (Tuesday). The first two weeks will be on Zoom (lectures and OHs).

• Please use Piazza for questions.

• To access to the Zoom link, Piazza, and Gradescope, you can find it in the course syllabus in this page.

• Lecture notes and recordings can be found in bCourse (recordings are not always guaranteed).

• There will be discussion sessions every other week.

Prerequisite

All students should have taken STAT 210A or an equivalent course in basic mathematical statistics, and must have a strong background in probability and real analysis. This course requires some degree of mathematical maturity.

Grading

• Class attendance is required.

• Each student is required to scribe at least 1 lecture. Please use this template for scribe. Please sign up scribing here.

• There will be 5-6 homeworks. Late submissions will get a deduction of 15 % per late day.

• In class mid-term. Date TBA.

• Final exam. Date Location TBA.

• Final grade will be Homework x 30 % + mid-term x 25 % + final x 40 % + scribe x 5 %.

Topics