MATH 5090: High-dimensional Data Analysis

Course Description

This course introduces several advanced classical and modern techniques for modeling and analysis of high-dimensional datasets with low-dimensional structures. The methods covered in this course include principal component analysis, factor analysis, clustering-based methods, and sparse and low-rank recovery theory and algorithms. Topics are identical toMATH 4090but material is covered at a greater depth and additional projects/assignments are required. Credit cannot be earned for bothMATH 4090andMATH 5090.