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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 Trevor Ha. This book surveys many modern machine learning tools ranging from generalized linear models to SVM boosting different types of trees etc The presentation is 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 know Sometimes you understand this after doing a lot of research in that subject and coming back to the book Nevertheless the authors are great statisticians and know what they are talking aboutA word of caution I am not sure if this is a good book for self study if you don t have any background in machine learning or statistics A House of My Own Stories from My Life 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 Trevor Ha. This book surveys many modern machine learning tools ranging from generalized linear models to SVM boosting Under Her Command (The Bosss Pet, different types of trees etc The presentation is or less mathematical but the book El Gaucho Martín FierroLa vuelta de Martín Fierro does not provide a Fragonard Art and Eroticism deep analysis of why a specific method works Instead it gives you some intuition about what a method is trying to The Monarchs Are Missing details it summarizes all necessary and really important things you need to know Sometimes you understand this after Touchstone doing a lot of research in that subject and coming back to the book Nevertheless the authors are great statisticians and know what they are talking aboutA word of caution I am not sure if this is a good book for self study if you Chocolate Candy Always Melts In The Sun Poems AboutLove betrayal anger struggle and understanding don t have any background in machine learning or statistics

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The Elements of Stastical Learning Data Mining Inference and Prediction

Stie 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 wrote much of the statistical modeling software in 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 and projection pursui. An extremely well written introduction to machine learning I now understand why this is the universal textbook for machine learning classesThe 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 not hold your hand through implementation it does a fantastic job laying conceptual foundations I highly recommend this to any student serious in statistical thinking

SUMMARY The Elements of Stastical Learning Data Mining Inference and Prediction

During the past decade there has been an explosion in computation and information technology With it has 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. Download PDF at


10 thoughts on “The Elements of Stastical Learning Data Mining Inference and Prediction

  1. says:

    Download PDF at

  2. says:

    Excellent book Has repaid multiple rereadings and is a wonderful springboard for developing your own ideas in the area Currently I'm

  3. says:

    Well it was one of the most cha

  4. says:

    This book surveys many modern machine learning tools ranging from generalized linear models to SVM boosting different types of trees etc The presentation is or less mathematical but the book does not provide a deep analys

  5. says:

    I read this book for work during work but I'm falling behind my yearly goal so I'm including it on goodreads PTh

  6. says:

    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

  7. says:

    A detailed companion piece to the introductory ISLR this is an excellent introduction The only critiue would be that it is too even handed to in

  8. says:

    An extremely well written introduction to machine learning I now understand why this is the universal textbook for machine

  9. says:

    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 impo

  10. says:

    For the mathematician this book is too terse and hard to learn from to the point of pretentiousnessFor the software engineer the algorithms presentation in this book is poor A bunch of phrases with no clear state change step computations etcIn

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