AMS205: Introduction to Classical Statistical Learning

Introduction to classical statistical inference. Random variables and distributions; types of convergence; central limit theorems; maximum likelihood estimation; Newton-Raphson, Fisher scoring, Expectation-Maximization, and stochastic gradient algorithms; confidence intervals; hypothesis testing; ridge regression, lasso, and elastic net. Prerequisite(s): AMS 203. Enrollment restricted to graduate students.

5 credits

Year	Fall	Winter	Spring	Summer

Comments

Replacing old/canceled course AMS 205 (11/8/18 LK).

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