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41 of 45 people found the following review helpful
on April 13, 2011
Format: HardcoverVerified Purchase
This review is written from the perspective of a programmer who has sometimes had the chance to choose, hire, and work with algorithms and the mathematician/statisticians that love them in order to get things done for startup companies. I don't know if this review will be as helpful to professional mathematicians, statisticians, or computer scientists.

The good news is, this is pretty much the most important book you are going to read in the space. It will tie everything together for you in a way that I haven't seen any other book attempt. The bad news is you're going to have to work for it. If you just need to use a tool for a single task this book won't be worth it; think of it as a way to train yourself in the fundamentals of the space, but don't expect a recipe book. Get something in the "using R" series for that.

When it came out in 2001 my sense of machine learning was of a jumbled set of recipes that tended to work in some cases. This book showed me how the statistical concepts of bias, variance, smoothing and complexity cut across both fields of traditional statistics and inference and the machine learning algorithms made possible by cheaper cpus. Chapters 2-5 are worth the price of the book by themselves for their overview of learning, linear methods, and how those methods can be adopted for non-linear basis functions.

The hard parts:

First, don't bother reading this book if you aren't willing to learn at least the basics of linear algebra first. Skim the second and third chapters to get a sense for how rusty
your linear algebra is and then come back when you're ready.

Second, you really really want to use the SQRRR technique with this book. Having that glimpse of where you are going really helps guide you're understanding when you dig in for real.

Third, I wish I had known of R when I first read this; I recommend using it along with some sample data sets to follow along with the text so the concepts become skills not just
abstract relationships to forget. It would probably be worth the extra time, and I wish I had known to do that then.

Fourth, if you are reading this on your own time while making a living, don't expect to finish the book in a month or two.
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13 of 13 people found the following review helpful
on May 17, 2014
Format: HardcoverVerified Purchase
I have been using The Elements of Statistical Learning for years, so it is finally time to try and review it.

The Elements of Statistical Learning is a comprehensive mathematical treatment of machine learning from a statistical perspective. This means you get good derivations of popular methods such as support vector machines, random forests, and graphical models; but each is developed only after the appropriate (and wrongly considered less sexy) statistical framework has already been derived (linear models, kernel smoothing, ensembles, and so on).

In addition to having excellent and correct mathematical derivations of important algorithms The Elements of Statistical Learning is fairly unique in that it actually uses the math to accomplish big things. My favorite examples come from Chapter 3 "Linear Methods for Regression." The standard treatments of these methods depend heavily on respectful memorization of regurgitation of original iterative procedure definitions of the various regression methods. In such a standard formulation two regression methods are different if they have superficially different steps or if different citation/priority histories. The Elements of Statistical Learning instead derives the stopping conditions of each method and considers methods the same if they generate the same solution (regardless of how they claim they do it) and compares consequences and results of different methods. This hard use of isomorphism allows amazing results such as Figure 3.15 (which shows how Least Angle Regression differs from Lasso regression, not just in algorithm description or history: but by picking different models from the same data) and section 3.5.2 (which can separate Partial Least Squares' design CLAIM of fixing the x-dominance found in principle components analysis from how effective it actually is as fixing such problems).

The biggest issue is who is the book for? This is a mathy book emphasizing deep understanding over mere implementation. Unlike some lesser machine learning books the math is not there for appearances or mere intimidating typesetting: it is there to allow the authors to organize many methods into a smaller number of consistent themes. So I would say the book is for researchers and machine algorithm developers. If you have a specific issue that is making inference difficult you may find the solution in this book. This is good for researchers but probably off-putting for tinkers (as this book likely has methods superior to their current favorite new idea). The interested student will also benefit from this book, the derivations are done well so you learn a lot by working through them.

Finally- don't buy the kindle version, but the print book. This book is satisfying deep reading and you will want the advantages of the printed page (and Amazon's issues in conversion are certainly not the authors' fault).
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14 of 16 people found the following review helpful
on March 22, 2009
Format: HardcoverVerified Purchase
This is a quite interesting, and extremely useful book, but it is wearing to read in large chunks. The problem, if you want to call it that, is that it is essentially a 700 page catalogue of clever hacks in statistical learning. From a technical point of view it is well-ehough structured, but there is not the slightest trace of an overarching philosophy. And if you don't actually have a philosophical perspective in place before you start, the read you face might well be an even harder grind. Be warned.

Some of the reviews here complain that there is too much math. I don't think that is an issue. If you have decent intuitions in geometry, linear algebra, probability and information theory, then you should be able to cruise through and/or browse in a fairly relaxed way. If you don't have those intuitions, then you are attempting to read the wrong book.

There were a couple of things that I expected (things I happen to know a bit about), but that were missing. On the unsupervised learning side, the discussion of Gaussian mixture clustering was, I thought, a bit short and superficial, and did not bring out the combination of theoretical and practical power that the method offers. On the supervised learning side, I was surprised that a book that dedicates so much time to linear regression finds no room for a discussion of Gaussian process regression as far as I could see (the nearest point of approach is the use of Gaussian radial basis functions [oops: having written that, I immediately came across a brief discussion (S5.8.1) of, essentially, GP regression - though with no reference to standard literature]).
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8 of 9 people found the following review helpful
on December 15, 2009
Format: HardcoverVerified Purchase
"The Elements of Statistical Learning: Data Mining, Inference and Prediction," 2nd edition by Trevor Hastie, Robert Tibshirani and Jerome Friedman is the classic reference for the recent developments in machine learning statistical methods that have been developed at Stanford and other leading edge universities. Their book covers a broad range of topics and is filled with applications. Much new material has been added since the first edition was published in 2001. Since most of these procedures have been implemented in the open-source program R, this book provides a basic and needed reference for their application. Important estimation procedures discussed include MARS, GAM, Projection Pursuit, Exploratory Projection Pursuit, Random Forest, General Linear Models, Ridge Models and Lasso Models etc. There is an discussion of bagging and boosting and how these techniques can be used. There is an extensive index and the many of the datasets discussed are available from the web page of the book or from other sources on the web. Each chapter has a number of problems that test mastery of the material. I have used material from this book in a number of graduate classes at the University of Illinois in Chicago and have implemented a number of the techniques in my software system B34S. While the 1969 book by Box and Jenkins set the stage for time series analysis using ARIMA and Transfer Function Models, Hastie, Tibshirani and Friedman have produced the classic reference for a wide range of new and important techniques in the area of Machine Learning. For anyone interested in Data Mining this is a must own book.
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4 of 4 people found the following review helpful
on January 31, 2014
Format: HardcoverVerified Purchase
This is a graduate level overview of key statistical learning techniques. I bought it as I am currently a student in Hastie's graduate level stats course at Stanford. The book is fairly deep given it's breadth, which is why it's so long. It's a very good book and an amazing reference but I do not recommend it unless you have strong mathematical maturity. If you do not know linear algebra / probability well, you will be lost. If you're like me, you'll also have to do some googling around for some of the stats stuff he talks about (confidence intervals, chi squared distribution...). I personally don't mind this. The coverage of this book is truly amazing. Trevor talks about everything from the lasso and ridge regression to knn, SVM's, ensemble methods, and random forests. It's fun to just flip to a random page and just start reading. If you want to be an expert in this area, you need to have this book. On the other hand if you just want to know basic techniques and then apply them, this book might be overkill and you may be better served by a more elementary text.
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5 of 6 people found the following review helpful
on December 12, 2009
Format: Hardcover
The book is excellent if you want to use it as a reference and study machine learning by yourself. It's quite comprehensive and deep in areas in which authors are most familiar & famous (frequentist approach, ensemble techniques, maximum likelihood and its variations, lasso). I would recommend you Bishop's machine learning book as an alternative if you want to gain a deeper understanding of Bayesian techniques--that one is more readable as well. Hastie et al's book is just ok from a didactic point of view. The real world examples are complicated to follow (would prefer simpler synthetic data sets). Some descriptions & explanations are too terse--a price to pay for comprehensiveness in a small volume. Overall, a great effort and useful contribution. You'll most likely need to check out other sources to gain a deeper understanding of some of the topics discussed. See MIT machine learning lecture notes (available online).
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4 of 5 people found the following review helpful
on August 26, 2010
Format: Hardcover
This book is one of the classics when it comes to the field of statistics and data mining. It provides a good mix of theory and practice in a concise manner - for statisticians and mathematicians at least.

The good teaching will make you understand the concepts of a huge variety of methods. Digging deeper you will probably need to consult a more specialized source for the particular method of interest.
Take a look at the table of contents for an overview.

The color print makes the book very visually appealing.

Note that the book can now just be downloaded!
[...]
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2 of 2 people found the following review helpful
on May 26, 2013
Format: HardcoverVerified Purchase
I would recommend this book to those who need to use machine learning. It is great as a reference book. It's compact style means that it will most benefit those with a background of linear algebra (matrices) and some calculus. It may not be the best book to start learning these techniques from scratch.
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8 of 11 people found the following review helpful
on January 16, 2013
Format: Hardcover
In 2009, the second edition of the book added new chapters on random forests, ensemble learning, undirected graphical models, and high dimensional problems. And now, thanks to an agreement between the authors and the publisher, a PDF version of the 2nd edition is now available for free download. [...]
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1 of 1 people found the following review helpful
on December 3, 2013
Format: Hardcover
This book is used by many machine learning courses. It is used in the Stanford grad program, which should give everyone enough understand of the authors targeted audience. Do not expect to sit down and just read it like a novel for a quick overview of statistical learning methods. Warning, expect some heavy duty math. In the interest of full disclosure I'll repeat: expect mega-math. The authors claim you can read the book and avoid what they term "technically challenging" sections, but I'm not really sure how one would do that. The book presents just about every important ML technique from decision trees to neural nets and boosting to ensemble methods. The Bayesian neural nets are tons of fun.

You can download a pdf copy from the authors website to take a look at it, but a serious student in the subject really should get hardcopy. [...]
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