Introduction to High Dimensional Data Analysis

MATH465

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: MATH 221.

Crosslistings
  • COMPSCI445
Curriculum Codes
QS