Course: STAT 27700=CMSC 25300
Title: Mathematical Foundations of Machine Learning
Instructor(s): Rebecca Willett
Teaching Assistant(s): TBA
Class Schedule: Sec 01: TR 11:20 AM–12:40 PM in TBA
Textbook(s): Eldén, Matrix Methods in Data Mining and Pattern Recognition (recommended)
Boyd, Vandenberghe, Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares (available online)
Description: This course is an introduction to the mathematical foundations of machine learning that focuses on matrix methods and features real-world applications ranging from classification and clustering to denoising and data analysis. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Students are expected to have taken calculus and have exposure to numerical computing (e.g. Matlab, Python, Julia, R).
Prerequisite(s): CMSC 12200 or CMSC 15200 or CMSC 16200, and the equivalent of two quarters of calculus (MATH 13200 or higher).