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COMP_SCI 496: High-Dimensional Probability for Computer Science

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Prerequisites

CS PhDs or Instructor permission

Description

The main goal of this course is to introduce core ideas and techniques from high-dimensional probability, with an emphasis on applications in algorithms and machine learning. The course covers concentration of measure, random matrices, random walks and Markov chains, optimal transport, and stochastic differential equations. Each topic is developed alongside representative algorithmic applications, including algorithmic statistics, learning algorithms, sketching and dimensionality reduction, efficient sampling methods, and diffusion-based generative models. 

INSTRUCTOR: Prof. Sidhanth Mohanty