AMS205: Introduction to Classical Statistical Learning

*****COURSES ARE SUBJECT TO CHANGE*****

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

YearFallWinterSpringSummer
2018-19
2007-08
2006-07
2005-06
2004-05
2003-04
2002-03
Comments

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

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