Description: This course is an introduction to concepts, methods, best practices, and theoretical foundations
of machine learning. Topics covered include regression, classification, kernels, dimensionality
reduction, clustering, decision trees, ensemble learning, regularization, learning
theory, and neural networks. Recommended Prerequisite(s): CAAM 334 or CAAM 335 or MATH 355 Mutually Exclusive: Cannot register for DSCI 303 if student has credit for ELEC 478/ELEC 578.