Syllabus

Course Meeting Times

Lectures: 2 sessions / week, 1.5 hours / session

Prerequisites

Course Description

This course offers an introduction to the finite sample analysis of high-dimensional statistical methods. The goal is to present various proof techniques for state-of-the-art methods in regression, matrix estimation and principal component analysis (PCA) as well as optimality guarantees. The course ends with research questions that are currently open.

Schedule

  • Sub-Gaussian Random Variables - 2 weeks
  • Linear Regression - 3 weeks
  • Misspecified Linear Models and Nonparametric Regression - 2 weeks
  • Matrix Estimation - 3 weeks
  • Minimax Lower Bounds - 3 weeks

Notes

This course has no required or recommended textbooks but notes are provided.

Problem Sets

There will be two problem sets.

Grading Policy

ACTIVITIES PERCENTAGES
Homework 30%
Midterm 20%
Final Exam 50%

Course Info

Learning Resource Types

assignment Problem Sets
notes Lecture Notes