Examine principles of unsupervised and supervised learning; exploratory data analysis; feature engineering; predictive modeling for regression and classification; clustering algorithms; neural networks and stochastic gradient descent methods; scalable models for Big Data sets; case studies; and programming predictive models in Python and R. Only one of following may be counted: Electrical and Computer Engineering 461P, Electrical Engineering 361M, 461P. Three lecture hours and one recitation hour a week for one semester.