High Dimensional Probability with Algorithmic Applications. (3-0) 3 Credit Hours

Catalog description

Prerequisite: MAT 5163 and MAT 5283 or equivalents. Topics may include: basic inequalities for random variables, Concentration Inequalities: Sub-gaussian and sub-exponential concentration, Chernoff, and Hoeffding. Applications to random networks. High-dimensional vectors: covariance estimation and Principal Component Analysis. Sub-gaussian vectors in high dimensions. Applications to randomized rounding in discrete optimization. Random matrices: net arguments, tail bounds, applications to numerical analysis, community detection, and error correcting codes. Stochastic processes: Gaussian width and basic inequalities for supremum of stochastic processes and applications. This course has Differential Tuition. Course Fee: GS01 $90.