Mathematical Statistics

Catalog description

This course provides rigorous tools for the mathematical analysis of statistical procedures in data science. Topics include hypothesis testing, confidence sets, regression, classification, and non-parametric statistics. We will focus both on classical asymptotic theory and on modern non-asymptotic techniques and theorems suitable for data science applications. Prerequisites: linear algebra, probability, comfort with mathematical proofs.