The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) 2nd Edition by Trevor Hastie | (PDF) Free Download

Description

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing 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 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 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 factorisation, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐I learned a little bit statistical learning before. But this book makes me question myself if I do understand what I thought I did.

⭐While expansive and overall just good, this is not great, and should not be the go-to book for people interested in learning machine learning. There’s lots of “it’s easy to show…” and lots of formulas just plopped down. For people interested in actually learning where stuff comes from in a complete and satisfying way, this is not a good book. This would be fine if this book was fantastic on the applied front, but it’s absolutely not. This is a good book for people who want the basic idea and some good pointers/details along the way, but aren’t looking for hardcore theory nor in the weeds applications. I’m not even sure I’d recommend it despite it’s being good.

⭐I like this book but with some reservations.For an intermediate or advanced student, this is a great book for expanding your toolkit — it discusses and explores many techniques with which you are probably already familiar, and plenty more with which you are probably not. This book has a smorgasbord of in-depth explorations of a wide array of useful techniques. So it’s great as a reference, and a great read for any practitioner looking to add more tools to their toolbox (and aren’t we all looking for that)?As noted, though, I have a few reservations. First, I wouldn’t recommend this for the beginner — many of the derivations skip some steps that are obvious if you’ve seen this problem before, but not so much if you’re seeing it for the first time, and the importance and area of application of each technique isn’t always made clear. This is a good book to read, but it shouldn’t be your first.Second, some of the material is a little…dated. The material on neural networks is so dated it’s basically not useful — doesn’t discuss any of the more recent advances (dropout, batch normalization, LSTMs etc), and same goes for any material in here related to image recognition. (Nearest neighbors with handcrafted features for image recognition? seriously? What about CNNs?) Of course that’s probably just a function of when this was written.Keeping those reservations in mind, though, this book will give you a thorough grounding in a wide array of powerful techniques for analyzing your data. You’ll want this on your shelf as a reference at the very least.

⭐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).

⭐Some context first: I’m studying my fourth year in a computer engineering program, having studied lightweight mathematics courses only, which is basically calculus, linear algebra, discrete mathematics and matematical statistics. Our machine learning course has two recommended literatures of which “The Elements of Statistical Learning” (ESL) was one of them, while the primary was Pattern Recognition and Machine Learning (PRML).My experience with the book so far if very positive. It contains incredibly relevant machine learning methods/tools which many other books, most notably PRML, doesn’t touch upon or at least explain very shortly, which are extensively used in practice. Most notably: Support Vector Machines, Random Forests and Ensemble Learning. Also, the structure of ESL has made a lot more sense to me compared to PRML, it wraps parts of the field into more easily digestible chunks, and therefore makes for a better reference than PRML (just compare the table of contents). Also, as the authors themselves point out, the book itself will rather the reader understands the intuition, algorithm and the cases in which they perform good/bad rather than the mathematical background/proofs behind them (don’t worry, most of them are still presented in ESL though). In conclusion, if you can accept the skimming of proof and some rigour in ESL, this book is perfect, and summarizes a large part of the field in such a way that even a mathematically mediocre computer scientist is able to somewhat grasp and apply in real world problems. However, if you want to get the entire picture, you might want to read both ESL and PRML, which will give you some of that Bayesian goodies as well.

⭐Having completed the Coursera Stanford Machine Learning course I wanted to know more and this came up at the top recommended book in Amazon for ML. I downloaded the free PDF but it’s huge and I find it impossible to read a PDF on a screen so I forked out for the hardback paper copy. I have to say this is well worth it, incredible scope of coverage and the colouring makes it more easy to understand (none of this stuff is actually ‘easy’). This IMO is genuinely THE bible for Machine Learning.

⭐There are a lot of typos in this text especially in the equations, e.g., “(3” where there should be the greek letter beta. Clearly not enough attention to proof reading by the publisher … I would return the kindle book but the time limit for that is exceeded in Amazon! So I complain in this review instead. The Google books version seems to be much better …

⭐As many other reviews have covered, this is an important text book, and covers a wide array of topics in suitable detail. I have subtracted two stars due to the atrocious print quality, some of the references cannot be read as they are so blurry, the spine is coming apart, and the pages are bound unevenly. The book almost seems like a fake copy…

⭐You need to have very great mathematical basis to understand many content in this book, It’s a very good one if you want a deeper insight of reinforcement learning.

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