Course: STAT 27700
Title: Mathematical Foundations of Machine Learning
Instructor(s): Rebecca Willett
Teaching Assistant(s): Takintayo Akinbiyi and Bumeng Zhuo
Class Schedule: Sec 01: MW 3:00 PM–4:20 PM in Ryerson 251
Sec 02: MW 9:00 AM-10:20AM in Crerar Library 011
Office Hours:
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 here)
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 11900 or 12200 or CMSC 15200 or CMSC 16200.
