EE 516 Mathematical Foundations of Machine Learning
The goal of this course is to move from familiarity to fluency with the use of linear algebra to solve problems in machine learning and signal processing. Topics covered include least squares, the singular value decomposition, eigenvalue decomposition, subspace methods, and optimization methods such as stochastic gradient descent, momentum methods, ADMM, and iteratively reweighted least squares. Programming experience in a high-level language (Matlab or Python) and familiarity with calculus is required.
Prerequisite
Graduate standing or instructor permission.