Course Meeting Times
Lectures: 2 sessions / week, 1.5 hours / session
Prerequisites
- 18.100C Real Analysis
- 18.700 Linear Algebra
- 18.440 Probability and Random Variables (Conditional Expectations, Random Vectors)
- 18.466 Mathematical Statistics (Statistical Model, Exponential Families, Estimation, Confidence Intervals)
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% |
