Machine Learning

Catalog description

An introduction to the theory and practice of machine learning, with a focus on mathematical foundations and real-world applications. Topics include supervised and unsupervised learning, Bayesian decision theory, nonparametric methods, neural networks, and clustering. Students gain hands-on experience with data-driven algorithms used across industry and research. Prerequisite: A grade of C or better in MATH 372 or MATH 351 or permission of instructor.