EE 460J: Data Science Laboratory

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, machine learning for signals and time-series data.

Course Level: 



The following with a grade of at least C- in each: Mathematics 340L; and Computer Science 314, 314H, or Electrical Engineering 360C; and Biomedical Engineering 343 or Electrical Engineering 313; and Biomedical Engineering 335, Electrical Engineering 351K, or Mathematics 362K. Credit with a grade of at least C- or registration for Aerospace Engineering 333T, Biomedical Engineering 333T, Chemical Engineering 333T, Civil Engineering 333T, Electrical Engineering 333T, Mechanical Engineering 333T, or Petroleum and Geosystems Engineering 333T.