✓ The Elements of Statistical Learning: Data Mining, Inference, and Prediction || ↠ PDF Download by ☆ Trevor Hastie Robert Tibshirani Jerome Friedman

By Trevor Hastie Robert Tibshirani Jerome Friedman | Comments: ( 517 ) | Date: ( Nov 12, 2019 )

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 learninDuring 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.


  • Title: The Elements of Statistical Learning: Data Mining, Inference, and Prediction
  • Author: Trevor Hastie Robert Tibshirani Jerome Friedman
  • ISBN: 9780387848570
  • Page: 254
  • Format: Hardcover

About Author:

Trevor Hastie Robert Tibshirani Jerome Friedman

Trevor Hastie Robert Tibshirani Jerome Friedman Is a well-known author, some of his books are a fascination for readers like in the The Elements of Statistical Learning: Data Mining, Inference, and Prediction book, this is one of the most wanted Trevor Hastie Robert Tibshirani Jerome Friedman author readers around the world.



Comments The Elements of Statistical Learning: Data Mining, Inference, and Prediction

  • Mauricio Vieira

    Download PDF at www-statanford/~tibs/El


  • Clif Davis

    Excellent book. Has repaid multiple rereadings and is a wonderful springboard for developing your own ideas in the area. Currently I'm going through Additive Models again which I breezed by the first few times. The short section on the interplay between Bias, Variance and Model Complexity is one of the best explanations I've seen. After retiring, I developed a method of learning a variation of regression trees that use a linear separation at the decision points and a linear model at the leaf nod [...]


  • Kirill

    Well, it was one of the most channeling books I've read in my career. It is a rigorous and mathematically dense book on machine learning techniques.Be sure to refine your understanding of linear algebra and convex optimization before reading this book. Nonetheless, the investment will totally worth it.


  • Amir-massoud

    This book surveys many modern machine learning tools ranging from generalized linear models to SVM, boosting, different types of trees, etc. The presentation is more or less mathematical, but the book does not provide a deep analysis of why a specific method works. Instead, it gives you some intuition about what a method is trying to do. And this is the reason I like this book so much. Without going into mathematical details, it summarizes all necessary (and really important) things you need to [...]


  • Wooi Hen Yap

    A classic text in machine learning from statistical perspective. No matter you're a novice machine learning practitioner, undergrad or hardcore PhD you can't miss out on this one. Overall, a good nontrivial broad intro to machine learning without loss of technical depth.


  • Dan Boeriu

    For the mathematician - this book is too terse and hard to learn from to the point of pretentiousness.For the software engineer - the algorithms presentation in this book is poor. A bunch of phrases with no clear state change, step computations, etc.In general - a lot of pompous presentations and hand waiving material.Something positive: the paper is top quality.


  • Wojtekwalczak

    Nice as a reference or an overview, but not necessarily as a source for learning. So many approaches and techniques are described in this book, that out of necessity, their description is very general, very condensed and very mathematical.


  • Jin Shusong

    Everyone in machine learning area should read it.


  • Razvan Coca

    It sounds like the right perspective on Machine Leaning


  • Rodrigo Rivera

    The Elements of Statistical Learning (ESL) ist ein Standardwerk für Maschine Learning. Die Autoren sind sehr renommierte Frequentists und sind seit Jahrzehnten Statistik-Professoren an der Stanford University. Im Buch ist dies sehr deutlich zu sehen. Das Buch geht sehr tief in die Materie und erfordert Konzentration und Zeit. Es wird alles behandelt und bewiesen.Die Mathematikvorkenntnisse sind deswegen relativ hoch. Deswegen wird dieses Buch sehr häufig für ML-Vorlesungen auf Master-Niveau w [...]


  • Jason Yang

    An extremely well-written introduction to machine learning. I now understand why this is the universal textbook for machine learning classes.The math is described at a reasonably high level, but the authors do a fantastic job emphasizing the conceptual differences between different learning algorithms. A major focus of this text is on conditions which favor some algorithms over others in minimizing variability for different learning exercises. While this book is not a very pragmatic text (does n [...]


  • Alex

    It's a classic, but it's not my favorite text at this level for either teaching or self-study. Coverage of core methods is relatively good, but the content sometimes veres between highly mathematical and formulaic, missing important conceptual areas. I wouldn't consider a statistics/ML/bioinformatics/ library complete without ESL, but I think Pattern Recognition and Machine Learning is a better overall resource and aid to teaching this content.


  • David

    Great book covering the principles of applied statistical learning. The book's mathematical rigor is semi-formal, opting for intuitive explanations and keeping proofs to a minimum. Chapters contain a thorough treatment of their subject, touching on modern research topics.


  • Kyle Hart

    The best explanation of additive models and piecewise approaches to linear modeling that I've found. The section on b-splines is especially good. The book is a treasure--one of my favorites. Worth keeping on the shelf to go back to over and over.


  • Darin

    The topics are described more from a statistics perspective than the computer science perspective, but as written by statisticians for computer scientists instead of for other statisticians. The examples are interesting and the graphics very nice.


  • Tianlin

    This book shows a nice statistical foundation of modern machine learning. Due to the rapid development of this field, the book seemed a bit out-dated. The notations are sometimes messed up too. But anyway, it reveals a unique statistical perspective of learning and is quite interesting per se.


  • Scott

    A classic! One of the first books I read on Machine Learning. Comes at things from the statistics perspective, probably wouldn't recommend as a first introduction. Also would recommend the updated electronic editions (freely available form Hastie's webpage


  • Ilknur

    This book can be downloaded freely from the authors' web page.


  • Xiaolin

    A great book that completely change my mind about statistics, machine learning.


  • Theresamvitolo

    C:\Users\Theresa\Documents\My Docs\learn coursea\statistical learning book


  • Danial

    Unnecessarily dry and difficult to read through; but as a reference book with a solid index it hits its mark.


  • a33eponine

    One of the more useful stat machine learning books I've read. Correct authors are Hastie, Friedman, and Tibshirani.


  • Abhilash Kulkarni

    The best book for an in depth understanding of pattern recognition and statistical learning.


  • Jane

    Requires a very thorough grasp of linear algebra. A little too complex for my level of understanding.


  • Nate Yoder

    Good introduction to the overall topic but little to no help on the actual implementation of the algorithms.


  • Jesus Angulo

    Starting the journey!!


  • Yusuke

    Great reference


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  • ✓ The Elements of Statistical Learning: Data Mining, Inference, and Prediction || ↠ PDF Download by ☆ Trevor Hastie Robert Tibshirani Jerome Friedman
    254 Trevor Hastie Robert Tibshirani Jerome Friedman
  • thumbnail Title: ✓ The Elements of Statistical Learning: Data Mining, Inference, and Prediction || ↠ PDF Download by ☆ Trevor Hastie Robert Tibshirani Jerome Friedman
    Posted by:Trevor Hastie Robert Tibshirani Jerome Friedman
    Published :2019-08-17T01:59:27+00:00