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: Mathematics 218 or 221.
Instructor
Bendich, Paul
Time/Location
TuTh 8:30am-9:45am
Gross Hall 318