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 | | Machine Learning by Tom M. Mitchell ISBN-10: 0070428077 ISBN-10: 0-07-042807-7 ISBN-13: 9780070428072 ISBN-13: 978-0-07-042807-2 Hardcover 1997-03-01 McGraw-Hill Science/Engineering/Math Find Lowest Price |
Editorials | ||
Product Description This exciting addition to the McGraw-Hill Series in Computer Science focuses on the concepts and techniques that contribute to the rapidly changing field of machine learning--including probability and statistics, artificial intelligence, and neural networks--unifying them all in a logical and coherent manner. Machine Learning serves as a useful reference tool for software developers and researchers, as well as an outstanding text for college students. | ||
Reviews | ||
Excellent Book, but for Academia Only This book is a redaction of many different white papers on the topic of machine learning. The material is very credible and accepted in the field, with very little (if any) temporal information (short term at least). With that said, it is also very dry and academic, and requires a solid background in mathematics to understand. Even if you are in the field, you're likely to read certain pages several times to embrace a concept... but once you embrace it, you will have some of the best foundational knowledge there is on the subject. If you're in the machine-learning field, you'll benefit from revisiting some of these subject, and probably learn a new thing or two. | ||
Outstanding I read this book about 7 years ago while in the PhD program at Stanford University. I consider this book not only the best Machine Learning book, but one of the best books in all of Computer Science. It covers every branch of ML I know of and it covers it really well. I found Mitchell's chapter on Neural Networks more insightful than an entire book on NN's that I read. I also found his chapter on Reinforcement Learning more useful and better explained than an entire book on Reinforcement Learning that I also read. The other chapters cover other ML topics at the same level of quality and rigor. The author did an amazing job in covering the breadth and depth of ML in less than 500 pages. I hope he will write a new edition to cover the advances that happened in the last decade. | ||
Great Start to Machine Learning I have used this book during my masters and found it to be an extremely helpful and a gentle introduction to the thick and things of machine learning applications. The various chapters are nicely paced with helpful problems at the end. Another great thing about the book is treatment of detailed examples with each concept and that the author carefully ties various concepts as they arise, with not just new, but also examples from previous chapters, which helps the user to understand different concepts applied to same problems thereby making clear difference between different methods. Also the author has a dedicated website with updated errata and notes, which is also very helpful! Having said that, I think the book is an introduction to various machine learning methods and one can easily follow on the references listed for detailed treatment of relevant topics. | ||
Best book I've seen on topic I have this book listed as one of the best and most interesting I've ever read. I loved the book just as much as I loved the course we used it in. I have a genuine interest in AI and especially Machine Learning and this book both inspired me as well as clared some things up for me. Like how the spectrum of different known methods differ in their appoach of different problems. This book is also very concise and well written, without being too mathematical. Making it very easy to read and understand. Ever since I took that course I keep returning to this book as a reference when I have a related problem to solve, or just bothering my mind. Highly recommended! | ||
too expensive I would say great book if you wanna start sth anywhere in machine learning, but it is toooooo expensive. | ||