Machine Learning

Catalog description

This course teaches students machine learning theory and algorithms. Students will learn about probably approximately correct (PAC), empirical risk minimization (ERM), structural risk minimization (SRM), and minimum description length (MDL) learning rules. Students will then study various machine learning algorithms, such as linear models, gradient descent, support vector machines (SVM), kernel methods, and trees, and how they connect to the theoretical framework. Finally, the course culminates with additional topicssuch as clustering, dimensionality reduction, generative models, and feature selection. Restrictions/Requirements: Open to Data Science Majors.