Prerequisitesįirst courses in statistics, linear algebra, and computing.
#An introduction to statistical learning r pdf
As of January 5, 2014, the pdf for this book will be available for free, with the consent of the publisher, on the book website. The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013).
There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter. We focus on what we consider to be the important elements of modern data analysis. This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense. An Introduction to Statistical Learning: With Applications in R. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical). Books similar to An Introduction to Statistical Learning: With Applications in R. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso) nonlinear models, splines and generalized additive models tree-based methods, random forests and boosting support-vector machines. This is an introductory-level course in supervised learning, with a focus on regression and classification methods.
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