This course covers a number of statistical methods that are commonly referred to as "machine learning." These include methods for both regression and classification. Topics include nonparametric regression, including k-NN, kernel regression, and splines; regularized regression, including lasso, ridge regression, and elastic net; methods based on logistic regression; tree-based methods, including random forests, bagging, and boosting; support vector machines; and neural networks.
Students will be expected to understand the key ideas of the methods that are discussed and to become proficient at using several of them to analyze actual datasets. There will be three empirical assignments and a more substantial empirical project.
The course will make considerable use of the book
"An Introduction to Statistical Learning with Applications in R",
by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani,
Second Edition, Springer, 2021.
Note that a PDF version of this book can be obtained (legally) on-line.
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