36-711 High Dimensional Probability and Applications

6 units, Carnegie Mellon University

In this course, we will introduce non-asymptotic methods in high-dimensional probability that find common use in applications across statistics, computer science, data science, and engineering. Topics include tail bounds for i.i.d. sums and martingale differences, concentration inequalities for non-linear functions, matrix concentration, and suprema of stochastic processes.

Prerequisites: none listed

Offered: Fall 2026, Fall 2025

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