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Binding: PaperbackEAN: 9780071154673 Edition: 1st ISBN: 0071154671 Label: McGraw Hill Higher Education Manufacturer: McGraw Hill Higher Education Number Of Pages: 352 Publication Date: October 01, 1997 Publisher: McGraw Hill Higher Education Studio: McGraw Hill Higher Education Editorial Review: Product Description: This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning. Average Rating:
 Rating:  - Please bow down to Tom MitchellThis is not my favorite machine learning book, but Tom Mitchell did us all a favor by writing it. It covers the breadth of topics that make up the machine learning discipline fairly completely. Since this book is about completely, there is also a shallowness, but that shallowness does not trim out complete descriptions of the algorithms covered. Oh no, all the gory math is there, what isn't there are simple examples. My first time through the book, what gave me the biggest headache ... Read More Rating:  - Good presentation of conceptsThe book machine learning by Mitchell provides a systematic overview of important concepts in the field. This is rather rare finding because most books present first of all algorithms but fall short communicating systematic insights that would help the reader to creatively develop methods by themselves. It is needless to say that any book with the title 'machine learning' is inherent incomplete due to the incompletenss of the field itself. For this reason this book is not state ... Read More Rating: - Excellent Book, but for Academia OnlyThis 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 ... Read More Rating: - OutstandingI 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 More Rating: - Great Start to Machine LearningI 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 ... Read More |
