Mathematical Foundations of Machine Learning

Catalog description

This course introduces students to the mathematical and statistical principles that form the foundation of modern machine learning. Topics include core concepts in probability, statistics, and linear algebra as they apply to machine learning algorithms, along with techniques such as linear regression, logistic regression, model selection, regularization, tree-based methods, support vector machines, and (time permitting) unsupervised learning. Emphasis will be placed on conceptual understanding, mathematical formulation, and hands-on problem-solving using real-world datasets. This course is designed for students with a strong interest in the mathematical aspects of data science and machine learning. Prerequisite Course(s): Prerequisites: MATH 220 (or CSCI 270), and MATH 310 Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3