Machine Learning (CSE)

3 credits · Graduate credit

Fundamentals of supervised, unsupervised, and sequential learning, including linear and nonlinear regression, logistic regression, support vector machines and kernel methods, decision trees, ensemble methods, neural networks and deep learning, dimension reduction, clustering, and probabilistic models. Emphasis on implementation and application to real-world data. Includes algorithms and derivations from fundamental principles.