Description: The course provides an introduction to concepts, methods, best practices, and theoretical foundations of machine learning. Topics covered include regression, classification, kernels, clustering, decision trees, ensemble learning, empirical risk minimization and regularization, and learning theory. Additional work is required for graduate students beyond the undergrad requirement.
Cross-list: ELEC 478. Mutually Exclusive: Cannot register for ELEC 578 if student has credit for ELEC 478.