• Presents mathematical statistics as a formal language for reasoning about data and uncertainty. Introduction to the basic framework of statistical decision theory, along with core concepts such as sufficiency, Bayes and minimax optimality of statistical procedures, with applications to optimal estimation, hypothesis testing, and prediction. Discussion topics include causality, multiple hypothesis testing, nonparametric and semiparametric statistics, and results for model misspecification. Targeted to students interested in statistical and machine learning research, with an emphasis on proofs and fundamental understanding.

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