MATH177 HM - Mathematical Methods in Data Science

Credit(s): 3

Instructor(s): Haddock

Offered: Fall

Description: In this course, students will learn about common mathematical representations of data, the mathematical foundations of matrix factorization and tensor decomposition, and their application to many tasks in machine learning and data science. These decomposition techniques are integral tools in studying large-scale and multi-modal data and form the basis for many approaches to the topic modeling, dimension reduction, and clustering tasks. Potential topics include PCA, nonnegative matrix factorization, higher-order SVD, nonnegative tensor decompositions, K-means clustering, optimization techniques for these models, and applications in machine learning, data science, signal processing, and network science.

Prerequisite(s): MATH019 HM (or equivalent course in multivariable calculus) and MATH073 HM (or equivalent course in linear algebra). MATH062 HM (or equivalent course in probability) is recommended but not required.