Prerequisite: EPIB300, EPIB315, EPIB650, or SPHL602.
Jointly offered with: EPIB667.
Credit only granted for: EPIB467 or EPIB667.
Machine Learning is the cutting-edge technology revolutionizing predictive analytics. This introductory course covers multiple models/algorithms widely applied to structured data with numerical and categorical variables, serving both as outcomes and predictors. It explores over a dozen foundational and advanced models for supervised learning, as well as several models for unsupervised learning. By covering essential math and statistics for understanding ML algorithms, the focus is on understanding the distinctive features of the models and hands-on coding exercises in the Cloud, emphasizing practical application from the start.
Machine Learning is the cutting-edge technology revolutionizing predictive analytics. This introductory course covers multiple models/ algorithms widely applied to structured data with numerical and categorical variables, serving both as outcomes and predictors. It explores over a dozen foundational and advanced models for supervised learning, as well as several models for unsupervised learning. By covering essential math and statistics for understanding ML algorithms, the focus is on understanding the distinctive features of the models and hands-on coding exercises in the Cloud, emphasizing practical application from the start.