Introduction to Foundations of Machine Learning

4 credits

Introduction to the mathematical foundations of machine learning models and algorithms, with core ideas motivated through examples from statistics, decision-making and control, communication and signal processing, and data science. Topics include linear algebra for data science, supervised learning methods such as linear regression and classification, and unsupervised learning methods such as clustering. Prerequisite: either MATH 136, MATH 208, or AMATH 352; and either E E 241 or CSE 163.