High-dimensional statistics

Lectures: 2 sessions / week, 1.5 hours / session

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.

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

There will be two problem sets.

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