IDS.160 / 18.S998 / 9.521 Spring 20

Mathematical Statistics: A non-asymptotic approach.


IDS.160 / 6.521 / 18.S998

Instructors: Sasha Rakhlin and Philippe Rigollet

TA:Felipe Suarez

Lectures: TR 1:00 - 2:30 pm in 2-190

Office Hours with Prof Rigollet: Tue. 2:30-3:30 in 2-279

Office Hours with TA Felipe Suarez Wed. 12:30-2:00 in 2-390 cluster

Course description

The course is an introduction to the non-asymptotic statistical analysis of high-dimensional and nonparametric models. The goal is to prepare students for the fundamental notions of statistics required for research in statistics, statistical learning and related topics. Includes: linear and nonparametric regression, covariance estimation, principal component analysis, sparsity, minimax lower bounds, prediction and margin analysis for classification. We will develop a rigorous probabilistic toolkit, including tail bounds and a basic theory of empirical processes.

Target audience:

Graduate students with a solid grasp of probability. This course will satisfy the “Statistics” requirement for the Interdisciplinary Program in Statistics (IDPS). The course will not cover the classical topics (such as confidence intervals, hypothesis testing, decision theory, sufficiency, exponential families, etc). Students interested in these topics are encouraged to take 18.6501 and/or 18.655.

Course numbers:

The same class is offered under the following three course numbers: IDS.160, 9.521 and 18.S998. Students may register for either of these.


Resources (not required):