Four lectures on probabilistic methods for data science
Skip to main content
eScholarship
Open Access Publications from the University of California

Four lectures on probabilistic methods for data science

  • Author(s): Vershynin, Roman
  • et al.
Creative Commons 'BY' version 4.0 license
Abstract

Methods of high-dimensional probability play a central role in applications for statistics, signal processing theoretical computer science and related fields. These lectures present a sample of particularly useful tools of high-dimensional probability, focusing on the classical and matrix Bernstein's inequality and the uniform matrix deviation inequality. We illustrate these tools with applications for dimension reduction, network analysis, covariance estimation, matrix completion and sparse signal recovery. The lectures are geared towards beginning graduate students who have taken a rigorous course in probability but may not have any experience in data science applications.

Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.

Main Content
Current View