Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been ...
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Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications. This new textbook reacts these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as wellas researchers and practitioners, and assumes no previous knowledge of pattern recognition or - chine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
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Fast shipping, good brand new book and good print. Price is reasonable.
Dxxxx
Oct 30, 2008
As good as it gets
This book makes for terrible machine learning study material -- it's entirely equations, with no intuitive explanations or even real-life examples. It might be a good reference for someone who already has an intuitive understanding of the algorithms. Unfortunately, I've done some research and haven't found any books that are better overall. Tom Mitchell's book has a far better explanation of traditional machine learning methods, but it doesn't cover SVMs, which should be the meat of any modern machine learning course. I'd tell a friend to buy Mitchell's book instead, then using various papers, the site svms.org, and the tutorial by Burges to learn SVMs.
