Description
This course will introduce the fundamental concepts and algorithms that enable computational artifacts to modify and improve their performance through experience. We will cover a variety of topics, including decision trees, logistic regression, support vector machines, ensemble methods, Bayesian methods, neural networks, clustering, and dimensionality reduction. Prerequisite: PHYS 165 and MATH 216