Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. This book presents some of the most important modeling and prediction techniques, along with relevant applications. An Introduction to Statistical Learning: with Applications in R by Gareth James Synopsis: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
An Introduction to Statistical Learning: with Applications in R In addition, you must get at least half of the available exercise points, and likewise, at least half of the available exam points to pass. Please bring a laptop or a smartphone with you to the lectures so that you can complete the quizzes. They are done anonymously and they will not be graded. An Introduction to Statistical Learning.An Introduction to Statistical Learning: with Applications in R.An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
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