Machine Learning

Catalog description

This course introduces core machine learning models and algorithms for classification, regression, clustering, and dimensionality reduction. The course focuses on both understanding the theory of learning approaches and effectively using them to solve real-world data science problems. Topics include least squares methods, linear classification, support vector machines, Bayesian networks and inference, the EM algorithm, and kernel methods. Students discuss and examine how machine learning is used in the real world, including government data, business information, biomedicine, and cybersecurity. Students must achieve a minimum grade of B-. Prerequisite: DATA 546 with a minimum grade of B-.