[Télécharger] The Elements of Statistical Learning: Data Mining, Inference, and Prediction de Robert Tibshirani Livres En Ligne
Télécharger The Elements of Statistical Learning: Data Mining, Inference, and Prediction de Robert Tibshirani Francais PDF
Télécharger "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" de Robert Tibshirani Livres En Ligne
Auteur : Robert Tibshirani
Catégorie : Livres anglais et étrangers,Science,Mathematics
Broché : * pages
Éditeur : *
Langue : Français, Anglais
During 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. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates. 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.
Télécharger The Elements of Statistical Learning: Data Mining, Inference, and Prediction de Robert Tibshirani Livres Pdf Epub
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GitHub - maitbayev/the-elements-of-statistical-learning ~ My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman - maitbayev/the-elements-of-statistical-learning
The Elements of Statistical Learning / SpringerLink ~ 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 .
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The Elements of Statistical Learning: Data Mining ~ 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 .
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Introduction to Statistical Learning ~ Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science. The authors give precise, practical explanations of what methods .
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Robert Tibshirani — Wikipédia ~ Robert Tibshirani est un statisticien canado-américain né le 10 juillet 1956.Il est connu pour avoir développé la méthode du lasso, une méthode de régression pénalisée, pour avoir rédigé avec Bradley Efron un ouvrage de référence sur les méthodes de bootstrap, An Introduction to the Bootstrap, et pour avoir rédigé un ouvrage de référence en apprentissage automatique et data .
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Machine Learning 10-601 Spring 2015 ~ Machine Learning, Tom Mitchell. (optional) Pattern Recognition and Machine Learning, Christopher Bishop. (optional) The Elements of Statistical Learning: Data Mining, Inference and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman. (optional) Machine Learning: A Probabilistic Perspective, Kevin P. Murphy. (optional) Grading: Midterm .
Schedule - GitHub Pages ~ Reading. ISL: An Introduction to Statistical Learning by James, Witten, Hastie and Tibshirani ESL: The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Edition) by Hastie, Tibshirani, and Friedman ITDM: Introduction to Data Mining (Second Edition) by Tan, Steinbach, Karpatne, and Kumar MMDS: Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman
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