The pdf for this book is available for free on the book website. The exercises were solved using Python instead of R. The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013). An Introduction to Statistical Learning covers many of the same topics, but at a level. 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. Download the PDF textbook An Introduction to Statistical Learning with Applications in R (ISLR) to your favorite device for reading. clustering) If you wish to perform R exercises on your personal machine, download and install R. regression, classification) versus unsupervised learning ( e.g. This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. Common problem classes: supervised learning (e.g. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical). 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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