*****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
Year | Fall | Winter | Spring | Summer |
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2018-19 |
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2007-08 |
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2006-07 |
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2005-06 |
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2004-05 |
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2003-04 |
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2002-03 |
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