Understanding Machine Learning: From Theory to Algorithms

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		Understanding Machine Learning: From Theory to Algorithms
		Understanding Machine Learning: From Theory to Algorithms

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.

网友对Understanding Machine Learning: From Theory to Algorithms的评论

一本非常好的机器学习参考书；对online learning相关的内容讲解很不错。值得购买。

This book provides a great story line along with solid proofs of machine learning theories and algorithms.
Each chapter is rather short (15-20 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 coarse-to-fine 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 well-known 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 must-read book.

Very well written! This book emphasizes the essential principles of machine learning, useful both for reading theoretical papers in machine learning and putting the algorithms to work on real data.

This is an excellent introduction to machine learning which fills an important gap in the literature
by introducing students to formal broad conceptual frameworks for understanding, comparing, analyzing,
and designing large classes of popular machine learning algorithms. These frameworks are explicitly presented
as mathematical theorems but the authors are careful about explaining the underlying assumptions of key theorems and
interpreting the conclusions of such theorems. Richard M. Golden.

I love this book. It is an excellent compendium of detailed algorithms in machine learning.

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.

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