INTRO HIGH DIM DATA ANALYSIS

MATH465.01

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. Instructor: Staff

Prerequisite: 
Prerequisite: MATH 221.
Instructor: Bendich, Paul
Time: TuTh 8:30am-9:45am
Location: Gross Hall 304B