Mathematics of Data Analysis and Machine Learning

Catalog description

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. Students are not allowed to take both MATH 405 and STATS 302 because of the content overlap. Students who are planning to major in Data Science should take STATS 302 instead, and those who have taken MATH 405 may not major in Data Science. 001101