STAT205: 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 |
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2020-21 |
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2019-20 |
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