Understanding Machine Learning: From Theory to Algorithms 1st Edition by Shai Shalev-Shwartz (PDF)

Description

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.

User’s Reviews

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

⭐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 well-written and for the most part, easy to digest–a bit heavy on the math for what I was looking for, but you can skim over it for the ideas.

⭐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.

⭐Paperback book sell only at South Asia edition and shipped to California, USA. Zoom the picture to see edition details on bottom right corner.Is amazon authorized to sell this Edition to USA customers.Do not know any difference in edition content

⭐This book is a very well written. Doesn’t go so much into detail but it’s still very intuitive. Small chapters are very informative and keep you interested in the topics. I used this book for the undergraduate class in ML I taught and my students loved it. However, the paperback printing is awful. Very cheap papers and graphs are not colored. If you’d like to buy it just go with the original US edition of this book!Overall, the book is very nice for an introductory class in machine learning for an advanced undergraduate level class. Can also be used for a graduate level class but some other materials should be covered that are not included in this book.

⭐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.

⭐This book contains some good introductory insights and fundamental principles but quickly gets into a lot of esoteric theorems and corollaries. I am happy with the purchase but I do not think all its pages will turn out to be useful to me or the typical practioner. By the way, I received the South Asian (India) edition; not sure if the seller is trying to pull a fast one by substituting that, but condition and content seem equivalent to US edition.

⭐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.

⭐I bought this book as a reference for my PhD studies and in my opinion, it is the best Machine Learning textbook at upper undergraduate/graduate level.The book provides an elegant and lucid treatment of the most important result in the area of Statistical Learning Theory (SLT) along with a theoretically grounded explanation of the most common learning paradigms and algorithms. Authors made an effort of highlight and in formulating clearly, the key results in SLT enabling the reader to fix in mind the few key ideas. An examples of this is the discussion in 6.4 about the fundamental theorem of Learning Theory whose statement is separated in a qualitative and a quantitive version.Another feature of the book that I really appreciated is the that before presenting the proof of a results, the authors explained the key ideas allowing the reader to understand the proof techniques. I think this important for everyone who aims in doing research in SLT.Problems ranges from honing skill exercises to more involved one, however their solutions rarely requires more than what explained in the book. With this respect, the material is self-contained.Comparing this book with a similar but shorter book, ‘Learning from Data’ by Mustafa et al. I think the former is more complete and general covering a number of additional topics as dimensionality reduction, feature selection, clustering, a short intro to online learning and also more advanced theoretical concepts as Radamacher Complexities and PAC Bayes to mention a few. Furthermore, the latter does not cover the important topic of Structural Risk Minimization.A book with a similar coverage is ‘Foundations of Machine Learning’ by Mohri et al. , however I definitively prefer Understanding Machine Learning for the adoption of a consistent notation and the clarity of the mathematical arguments.

⭐The book delivers on the promise of the title. It is split into two parts: the first third dealing with a general theory of machine learning and the second two thirds applying the theory to understanding some well known ML algorithms. I mean ‘understanding’ in quite a specific way, and this is the strength of the book. For each algorithm the authors show how it fits within the general theory and how to use the theory to better understand the behaviour of the algorithm.The theory section is a little heavy on notation if you don’t have a mathematical background, but the actual mathematics employed is generally at a science undergraduate level, so don’t let that put you off. Also, they go to great lengths to explain the intuition behind the ideas. However, the ideas are quite abstract and it’s easy to wonder whether it’s really practical and worth understanding. Have faith, because when you get to the algorithm sections you will have some powerful tools to understand them better.If you’re tired of ML books that are little more than lists of algorithm recipes, give this book a try. It requires some serious investment in thought but it will pay you back handsomely.

⭐The book itself is very good, but the way it was edited for Kindle is just horrible. The equations are so small and do not scale when zooming, so following the derivations is a real strain on my eyes. I’d have returned the book but it’s past the 14 day cooling off period 🙁

⭐Apologies to the author for the low-rating.I love the content, however I was expecting colour, not sure if the the author had a say.I recommend just getting the pdf copy

⭐Il testo ricevuto, così come già scritto da altro recensore, NON è quello pubblicato. Quello che ho ricevuto ha moltissime pagine in meno ed è l’edizione per l’Asia. Inoltre lo consiglio solo a chi ha una fortissima base matematica. Ho provato a restituire il libro ma il venditore non pagherà l’intero importo versato. Venditore scorretto. Non mi aspettavo Amazon ospitasse venditore di questo tipo. E’ la prima volta che Amazon mi delude. E anche l’ultima

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