Matrix Methods in Machine Learning

ECE/CS/ME 532 (formerly “Theory and Applications of Pattern Recognition”)
University of Wisconsin–Madison

This course is an introduction to 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 a course in vector calculus, such as MATH 234, and have exposure to numerical computing (Matlab, Python, Julia, or equivalent). Appropriate for graduate students or advanced undergraduates.

Instructor: Laurent Lessard

IMPORTANT: The notes and videos below are from Fall 2017, which was the last time Prof. Lessard taught this course. More recent offerings of the course might use different notes/materials. The notes are provided for your reference only!

Course material Class Projects Resources