Explore predictive modeling, regression and classification, data cleaning and preprocessing, feature engineering, unsupervised methods, principal component analysis, data clustering, model selection and feature selection, entropy and information theory, neural networks, deep learning, and machine learning for signals and time-series data. Three lecture hours and three laboratory hours a week for one semester. Only one of the following may be counted: Electrical and Computer Engineering 460J, Electrical Engineering 460J, 379K (Topic: Data Science Laboratory), 379K (Topic 24). Prerequisite: The following with a grade of at least C- in each: Biomedical Engineering 343 or Electrical and Computer Engineering 313 (or Electrical Engineering 313), or 313H; and Biomedical Engineering 335, Electrical and Computer Engineering 351K (or Electrical Engineering 351K), 351H, or Mathematics 362K; and Mathematics 340L. Credit with a grade of at least C- or registration for Aerospace Engineering 333T, Biomedical Engineering 333T, Chemical Engineering 333T, Civil Engineering 333T, Electrical and Computer Engineering 333T (or Electrical Engineering 333T), Engineering Studies 333T, Mechanical Engineering 333T, or Petroleum and Geosystems Engineering 333T.