Understanding Machine Learning: From Theory To Algorithms
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*An electronic version of a printed book that can be read on a computer or handheld device designed specifically for this purpose.
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Supported Devices  Windows PC/PocketPC, Mac OS, Linux OS, Apple iPhone/iPod Touch. 
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Flowing Text / Pages  Pages 
Printable?  Yes 
Book details
 PDF  Unknown pages
 Shai ShalevShwartz(Author)
 Cambridge University Press; 1st edition (2015)
 English
 2
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Review Text
I bought it since I wanted to refresh my knowledge on machine learning (I am a CS graduate, took the ML course about 15 years ago...). I finished one third of it by now and enjoy it very much.What I especially like about this book is that it gives a good theoretical background, before jumping into the algorithms.When getting to the algorithms the author show how to use the theoretical tools to analyze them, which is great !Also, the theoretical part was enough for me to further read and understand more recent theoretical ML research papers.That is a great feeling ! I wholeheartedly recommend this great book for graduates.
I have read many of the main books on machine learning. This is hands down the best. Rather than a laundry list of techniques, the book starts with a concise and clear introduction to statistical machine learning and then consistently connects those concepts to the main ML algorithms. Each chapter is 10 pages or so of crisp math and lean prose. A brief summary at the beginning of each chapter gives a clear sense of what will be accomplished in it, and attention to notation makes sure that mathematics supports understanding rather than getting in the way. This is definitely not a "how to" book, but rather a "what and why" book, focused on understanding principles and connections between them. I read the book cover to cover, and I was left with a sense of machine learning as a coherent discipline, and a solid feel for the main concepts.
I love this book. It is an excellent compendium of detailed algorithms in machine learning.
Ideal book for learning theory of machine learning, in order to get a deeper understanding of practical algorithms. Clear mathematical presentation, covers every subject that I come over in articles and want to understand better, good exercises.
This is an excellent introduction to machine learning which fills an important gap in the literatureby introducing students to formal broad conceptual frameworks for understanding, comparing, analyzing,and designing large classes of popular machine learning algorithms. These frameworks are explicitly presentedas mathematical theorems but the authors are careful about explaining the underlying assumptions of key theorems andinterpreting the conclusions of such theorems. Richard M. Golden.
Approaching machine learning from the complexity point of view, it truly connects the algorithms to the computational complexity.
First, let me just say I regret purchasing the kindle version, as it is difficult to read the math symbols on the kindle, and even somewhat difficult to read them on the kindle for mac app on a big screen. Zoomed in leaves the symbols the same size (it appears as though they're images), with the surrounding text large. Perhaps this is a problem on most math texts, but I was disappointed.I'm enjoying the book. It reads like a textbook that one might find at a university, and has exercises and notes for the order you'd go through it while teaching a class. I find it wellwritten and for the most part, easy to digesta bit heavy on the math for what I was looking for, but you can skim over it for the ideas.
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This book provides a great story line along with solid proofs of machine learning theories and algorithms.Each chapter is rather short (1520 pages), yet is well written to convey the topic in detail, making the book comfortable to read.Moreover, the connection among consecutive chapters is strong, giving an excellent coarsetofine introduction on sophisticated theories.Over the past few years, I have read several machine learning books, and this is the one solidly based on "statistical learning theory".Compared to other books that give only brief description to this aspect, this book does a good job not only on providing the basic proofs, but also on extending the theories to wellknown practical algorithms, supporting the success of these algorithms and showing how theories can be used to design or analyze practical algorithms. For whom eager to know more about learning theory, this is a mustread book.