Course Description
Explore mathematical foundations of inferential and prediction frameworks, with emphasis on computation, used to learn from data. Frequentist, Bayesian, and Likelihood viewpoints are all considered. Topics: principles of estimation, optimality, bias, variance, consistency, sampling distributions, estimating equations, information, bootstrap methods, ROC curves, shrinkage, large sample theory, prediction optimality versus estimation optimality.
