Instructor: Professors Yury Polyanskiy (firstname.lastname@example.org), Devavrat Shah (email@example.com), and John Tsitsiklis, (firstname.lastname@example.org)
Schedule: Lec TR1-2:30, room 32-155; Recs: F11, F1, F2
Introduction to the methodological foundations of data science, emphasizing basic concepts, but also modern methodologies. Learning of distributions and their parameters. Testing of multiple hypotheses. Linear and nonlinear regression and prediction. Classification. Learning of dynamical models. Uncertainty quantification. Model validation. Causal inference. Applications and case studies drawn from electrical engineering, computer science, the life sciences, finance, and social networks.