Statistical Learning Elements

Featuring a variety of topics not found in the original, "The Elements of Statistical Learning" includes chapters on graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. It also delves into methods for handling "wide'' data (p bigger than n), such as multiple testing and false discovery rates.

This book presents important concepts across a range of disciplines, including medicine, biology, finance, and marketing, all within the framework of statistics. While the focus is on concepts rather than mathematics, numerous examples are provided, accompanied by colorful graphics. It serves as a valuable resource for statisticians and individuals interested in data mining in various fields.

Covering everything from supervised learning (prediction) to unsupervised learning, the book offers a comprehensive overview of the subject matter. Its wide-ranging topics make it an essential read for those looking to expand their knowledge in the field of statistical learning.

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