DS 6310 - Theory II: Inference & Prediction

Effective Date 09/28/2022
Explores the mathematical foundations of inferential and prediction frameworks commonly used to learn from data. Frequentist, Bayesian, Likelihood viewpoints are considered. Topics include: principles of estimation, optimality, bias, variance, consistency, sampling distributions, estimating equations, information, Bootstrap methods, ROC curves, shrinkage, and some large-sample theory, prediction optimality versus estimation optimality.

Credits: 3
Grading Basis Graded
Requisites Must be a Data Science PhD student