{"title":"Trevor Hastie","description":"Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R\/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.","products":[{"product_id":"the-elements-of-statistical-learning-data-mining-inference-and-prediction-second-edition-hardcover","title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition - Hardcover","description":"\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.\u003c\/p\u003e \u003cp\u003eThis major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression \u0026amp; path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for \"wide'' data (p bigger than n), including multiple testing and false discovery rates.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eDuring the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.\u003c\/p\u003e \u003cp\u003eThis major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (\u003cem\u003ep\u003c\/em\u003e bigger than \u003cem\u003en\u003c\/em\u003e), including multiple testing and false discovery rates.\u003c\/p\u003e \u003cp\u003eTrevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R\/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful \u003cem\u003eAn Introduction to theBootstrap\u003c\/em\u003e. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.\u003c\/p\u003e","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":51319130915039,"sku":"9780387848570","price":145.78,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0813\/8958\/4607\/files\/rN-tMpBdCw9780387848570.webp?v=1788366266"},{"product_id":"an-introduction-to-statistical-learning-with-applications-in-r-paperback","title":"An Introduction to Statistical Learning: With Applications in R - Paperback","description":"\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAn Introduction to Statistical Learning\u003c\/b\u003e 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. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.\u003c\/p\u003e\u003cp\u003eTwo of the authors co-wrote \u003ci\u003eThe Elements of Statistical Learning\u003c\/i\u003e (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. \u003cb\u003eAn Introduction to Statistical Learning\u003c\/b\u003e covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAn Introduction to Statistical Learning\u003c\/b\u003e 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. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.\u003c\/p\u003e\u003cp\u003eTwo of the authors co-wrote \u003ci\u003eThe Elements of Statistical Learning\u003c\/i\u003e (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. \u003cb\u003eAn Introduction to Statistical Learning\u003c\/b\u003e covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.\u003c\/p\u003e","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":51319185801439,"sku":"9781071614204","price":105.28,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0813\/8958\/4607\/files\/ShHADTzOUR9781071614204.webp?v=1788366368"},{"product_id":"an-introduction-to-statistical-learning-with-applications-in-python-hardcover","title":"An Introduction to Statistical Learning: With Applications in Python - Hardcover","description":"\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAn Introduction to Statistical Learning\u003c\/b\u003e 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, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. \u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eFour of the authors co-wrote \u003ci\u003eAn Introduction to Statistical Learning, With Applications in R\u003c\/i\u003e(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAn Introduction to Statistical Learning\u003c\/b\u003e 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, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eFour of the authors co-wrote \u003ci\u003eAn Introduction to Statistical Learning, With Applications in R \u003c\/i\u003e(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.\u003c\/p\u003e","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":51319191568607,"sku":"9783031387463","price":194.38,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0813\/8958\/4607\/files\/0yf_z_w5Is9783031387463.webp?v=1788366390"}],"url":"https:\/\/blackandbarhe.com\/collections\/trevor-hastie.oembed","provider":"Black \u0026 Barhe Bookstore","version":"1.0","type":"link"}