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. Four lecture hours a week for one semester. Only one of following may be counted: Electrical and Computer Engineering 461P, Electrical Engineering 361M, 461P. Prerequisite: The following with a grade of at least C- in each: Mathematics 340L; and Biomedical Engineering 343, Electrical and Computer Engineering 313 (or Electrical Engineering 313), or 313H; and Biomedical Engineering 335 or Electrical and Computer Engineering 351K (or Electrical Engineering 351K), 351H, or Mathematics 362K.