Mathematical Foundations of Machine Learning

Catalog description

This course introduces the fundamental concepts in inference and focuses on the concepts and algorithms central to machine learning. Students study the foundational mathematics needed for machine learning (linear algebra, analytic geometry, multivariate calculus), probability theory, and the essential methods for inference. These include maximum likelihood, naïve Bayes, Bayesian analysis, linear models, Bayesian linear models, and support vector machines. Credit Badge: Data Science