The Elements Of Statistical Learning

Author : Trevor Hastie,Robert Tibshirani,Jerome Friedman
Publisher : Springer Science & Business Media
Page : 545 pages
File Size : 50,9 Mb
Release : 2013-11-11
Category : Mathematics
ISBN : 9780387216065

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The Elements of Statistical Learning by Trevor Hastie,Robert Tibshirani,Jerome Friedman Pdf

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 should be 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.

Author : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani,Jonathan Taylor
Publisher : Springer Nature
Page : 617 pages
File Size : 47,6 Mb
Release : 2023-08-01
Category : Mathematics
ISBN : 9783031387470

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An Introduction to Statistical Learning by Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani,Jonathan Taylor Pdf

An Introduction to Statistical Learning 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. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (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.

Author : Trevor Hastie,Robert Tibshirani,Jerome H. Friedman
Publisher : Unknown
Page : 745 pages
File Size : 41,8 Mb
Release : 2009
Category : Biology
ISBN : 0387848843

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The Elements of Statistical Learning by Trevor Hastie,Robert Tibshirani,Jerome H. Friedman Pdf

Author : Jerome H. Friedman,Trevor Hastie,Robert Tibshirani
Publisher : Unknown
Page : 45 pages
File Size : 46,9 Mb
Release : 1998
Category : Regression analysis
ISBN : OCLC:40665647

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Additive Logistic Regression by Jerome H. Friedman,Trevor Hastie,Robert Tibshirani Pdf

Author : Larry Wasserman
Publisher : Springer Science & Business Media
Page : 446 pages
File Size : 50,5 Mb
Release : 2013-12-11
Category : Mathematics
ISBN : 9780387217369

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All of Statistics by Larry Wasserman Pdf

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.

Author : Peter Harrington
Publisher : Simon and Schuster
Page : 558 pages
File Size : 54,5 Mb
Release : 2012-04-03
Category : Computers
ISBN : 9781638352457

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Machine Learning in Action by Peter Harrington Pdf

Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce

Author : Vladimir Vapnik
Publisher : Springer Science & Business Media
Page : 324 pages
File Size : 47,9 Mb
Release : 2013-06-29
Category : Mathematics
ISBN : 9781475732641

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The Nature of Statistical Learning Theory by Vladimir Vapnik Pdf

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

Author : Brad Boehmke,Brandon M. Greenwell
Publisher : CRC Press
Page : 374 pages
File Size : 45,7 Mb
Release : 2019-11-07
Category : Business & Economics
ISBN : 9781000730432

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Hands-On Machine Learning with R by Brad Boehmke,Brandon M. Greenwell Pdf

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.

Author : Joe Suzuki
Publisher : Springer Nature
Page : 261 pages
File Size : 41,7 Mb
Release : 2021-08-03
Category : Computers
ISBN : 9789811578779

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Statistical Learning with Math and Python by Joe Suzuki Pdf

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.

Author : Trevor Hastie,Robert Tibshirani,Martin Wainwright
Publisher : CRC Press
Page : 354 pages
File Size : 47,9 Mb
Release : 2015-05-07
Category : Business & Economics
ISBN : 9781498712170

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Statistical Learning with Sparsity by Trevor Hastie,Robert Tibshirani,Martin Wainwright Pdf

Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underl

Author : Joseph K. Blitzstein,Jessica Hwang
Publisher : CRC Press
Page : 599 pages
File Size : 44,8 Mb
Release : 2014-07-24
Category : Mathematics
ISBN : 9781466575578

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Introduction to Probability by Joseph K. Blitzstein,Jessica Hwang Pdf

Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.

Author : Bradley Efron,Trevor Hastie
Publisher : Cambridge University Press
Page : 513 pages
File Size : 41,8 Mb
Release : 2021-06-17
Category : Computers
ISBN : 9781108823418

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Computer Age Statistical Inference, Student Edition by Bradley Efron,Trevor Hastie Pdf

Now in paperback and fortified with exercises, this brilliant, enjoyable text demystifies data science, statistics and machine learning.

Author : R. Tibshirani,J. Friedman
Publisher : Unknown
Page : 546 pages
File Size : 45,8 Mb
Release : 2001
Category : Artificial intelligence
ISBN : 1489905197

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The Elements of Statistical Learning by R. Tibshirani,J. Friedman Pdf

During the past decade there has been an explosion in computation and information technology.; With it has come a vast amount 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.

Author : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani
Publisher : Springer Nature
Page : 607 pages
File Size : 40,7 Mb
Release : 2021-07-29
Category : Mathematics
ISBN : 9781071614181

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An Introduction to Statistical Learning by Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani Pdf

An Introduction to Statistical Learning 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. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning 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. This 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.

Author : Trevor Hastie,Robert Tibshirani,Jerome H. Friedman
Publisher : Unknown
Page : 0 pages
File Size : 50,6 Mb
Release : 2023
Category : Algorithms
ISBN : 7519296865

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The Elements of Statistical Learning by Trevor Hastie,Robert Tibshirani,Jerome H. Friedman Pdf