MAT 430 - Mathematical Theory of Data Science

Mathematical formulation and derivation of established machine learning and data analysis methods, as well as applications. Topics include singular value decomposition, dimension reduction such as low-rank approximation, k-means, support vector machine, clustering and kernel methods, nonnegative matrix factorization and topic models, neural network and image classification.

Credit Hours: 3

Prerequisite Courses: MAT 261 and MAT 335
Course Repeatability: Course may not be repeated.