Introduction to High Dimensional Data Analysis

MATH 465

Geometry of high dimensional data sets. Linear dimension reduction, principal component analysis, kernel methods. Nonlinear dimension reduction, manifold models. Graphs. Random walks on graphs, diffusions, page rank. Clustering, classification and regression in high-dimensions. Sparsity. Computational aspects, randomized algorithms. Prerequisite: Mathematics 218 or 221. Instructor: Staff

Prerequisites

Prerequisite: Mathematics 218 or 221

Curriculum Codes
  • QS
Cross-Listed As
  • COMPSCI 445
  • STA 465
Typically Offered
Fall Only