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Understanding Machine Learning: From Theory to Algorithms

Ebook Free Understanding Machine Learning: From Theory to Algorithms

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Review

"This elegant book covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource, ideal for all those who want to understand how to find structure in data." Bernhard Schölkopf, Max Planck Institute for Intelligent Systems"This is a timely text on the mathematical foundations of machine learning, providing a treatment that is both deep and broad, not only rigorous but also with intuition and insight. It presents a wide range of classic, fundamental algorithmic and analysis techniques as well as cutting-edge research directions. This is a great book for anyone interested in the mathematical and computational underpinnings of this important and fascinating field." Avrim Blum, Carnegie Mellon University"This text gives a clear and broadly accessible view of the most important ideas in the area of full information decision problems. Written by two key contributors to the theoretical foundations in this area, it covers the range from theoretical foundations to algorithms, at a level appropriate for an advanced undergraduate course." Peter L. Bartlett, University of California, Berkeley

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Book Description

Machine learning makes use of computer programs to discover meaningful patters in complex data. It is one of the fastest growing areas of computer science, with far-reaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage. The authors explain the "hows" and "whys" of the most important machine-learning algorithms, as well as their inherent strengths and weaknesses, making the field accessible to students and practitioners in computer science, statistics, and engineering.

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Product details

Hardcover: 410 pages

Publisher: Cambridge University Press; 1 edition (May 19, 2014)

Language: English

ISBN-10: 1107057132

ISBN-13: 978-1107057135

Product Dimensions:

7.2 x 1.1 x 10.2 inches

Shipping Weight: 2 pounds (View shipping rates and policies)

Average Customer Review:

4.1 out of 5 stars

25 customer reviews

Amazon Best Sellers Rank:

#72,299 in Books (See Top 100 in Books)

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 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.

31 chapters in 360 pages, has exercises without answers.The test is very terse as you can figure out (each chapter has bibliography and exercises which take several pages from the chapter).From theory (no no) to algorithms well I little bit (same information level that I can get in Wikipedia)Just I want to know why k-means you can use Manhattan or euclidean distance, why to use one versus the otherthis book will not answer the question

For those with a strong mathematical background, this may be the best introduction out there. It takes one from the beginning concepts through to current research topics. Highly recommended!

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

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Understanding Machine Learning: From Theory to Algorithms PDF

Understanding Machine Learning: From Theory to Algorithms PDF