An Introduction to Statistical Learning: With Applications in Python (Springer Texts in Statistics) (Paperback)

An Introduction to Statistical Learning: With Applications in Python (Springer Texts in Statistics) By Gareth James, Daniela Witten, Trevor Hastie Cover Image
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About the Author

Gareth James is the John H. Harland Dean of Goizueta Business School at Emory University. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area. Daniela Witten is a professor of statistics and biostatistics, and the Dorothy Gilford Endowed Chair, at University of Washington. Her research focuses largely on statistical machine learning techniques for the analysis of complex, messy, and large-scale data, with an emphasis on unsupervised learning. Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University and are co-authors of the successful textbook Elements of Statistical Learning. Hastie and Tibshirani developed generalized additive models and wrote a popular book with that title. Hastie co-developed much of the statistical modeling software and environment in R, and invented principal curves and surfaces. Tibshirani invented the lasso and is co-author of the very successful book, An Introduction to the Bootstrap. They are both elected members of the US National Academy of Sciences. Jonathan Taylor is a professor of statistics at Stanford University. His research focuses on selective inference and signal detection in structured noise.

Product Details
ISBN: 9783031391897
ISBN-10: 3031391896
Publisher: Springer
Publication Date: September 8th, 2023
Pages: 607
Language: English
Series: Springer Texts in Statistics