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![]() Accounting & Finance Architecture Arts & Photography Business & Investing Business Management Artificial Intelligence Computers & Internet Circuitry Human-Computer Interaction Information Theory Modeling & Simulation Research Software Engineering Systems Analysis & Design Education Engineering History Humanities Law Medicine Professional Science Reference Science Social Sciences Summaries and Customer Reviews are supplied by Amazon.com
Customer Reviews:Average Customer Rating: Really an introduction I've read several parts of chapters which concerned my work and skimmed other chapters faster. This book should serve as a starting point and mostly as a quick introduction in a subject. However, i've found this book to be useful in other way - it is compact and I found several basic reasonements and assumptions quickly to base my conclusions in work i was doing. Also i like the style where key-words appear outside the text where they can be easily spotted and also the references at the end of each chapter. It is good it is very quick. I am in hong kong, but the product reach me in less than a week, it is in very good quality. Very good book This is a very good introduction to Machine Learning, but very terse at times. It's not superficial, but does not go too deep either. I think it's a good reference for a Machine Learning course (along with Tom Mitchell's book, maybe). Good overview of the field I bought this for use as a reference book rather than a textbook. I found it quite useful with just one proviso: the mathematical presentation goes very fast in places and may be too concise for some readers. Superb Organization of Ideas! The topics and concepts in this book are exceptionally well organized. After reading it from cover to cover, I could easily see how all the ideas and concepts fit into place. I have two main criticisms. First, the notation is sometimes non-standard, e.g. the r vector is used to denote the label vector and superscripts are used sometimes as subscripts. Second, the explanations are sometimes too brief. For example, when deriving the solution for Least Squares Regression with Quadratic Discriminants, Vandermode matrices are used but the author fails to identify them as such, or to explain why they are useful. If the author were to write an extra sentence on every other page, the explanations would be perfect! | | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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