MATH 432: Mathematics of Machine Learning
Prerequisites: A grade of P, C, or better in MATH 314 or MATH 315
Description: An introduction to the essential mathematical content necessary to understand machine learning, its opportunities, and its challenges. Differentiation in higher dimensions, gradient descent method, optimization over convex and non-convex domains, neural networks, and computer implementation of machine learning algorithms to solve benchmark problems.
Credit Hours: 3
This course will be taught by Professor Levi Heath on Mondays, Wednesdays, and Fridays at 9:30-10:30 am in 109 Avery Hall. Attached is a generic example syllabus for the course, and students can watch Professor Heath introduce and describe the course for the Fall 2025 semester here (starting at 13:41 and ending at 23:02). The MATH 432 course will count as an Advanced Math course for the Math major and minor, and it will be added to the Mathematical Modeling focus area of the Data Science major.
Please note: The MATH 432 course will be essentially the same course as the MATH 391 courses offered during the Spring 2024 Pre-Session and during the Spring 2025 Pre-Session, so students who completed either of those two MATH 391 courses should not take MATH 432.