Introduction to High-dimensional Data Analysis

Catalog description

Prerequisites: MATH 1320 , MATH 2000 and MATH 2450 . This course introduces several advanced classical and modern techniques for modeling and analysis of high-dimensional datasets with low-dimensional structures. The topics covered in this course include principal component analysis, factor analysis, clustering-based methods, and sparse and low-rank recovery theory and algorithms. Credit cannot be earned for both MATH 4090 and MATH 5090 .