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Pattern Recognition and Machine Learning

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 ?eld, 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 signi?cant impact on both algorithms and applications. This new textbook re?ects these recent developments while providing a comp- hensive introduction to the ?elds of pattern recognition and machine learning. It is aimed at advanced undergraduates or ?rst year PhD students, as well as 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 - sential as the book includes a self-contained introduction to basic probability theory.
Portrait


Chris Bishop is a Microsoft Distinguished Scientist and the Laboratory Director at Microsoft Research Cambridge. He is also Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, and in 2007 he was elected Fellow of the Royal Society of Edinburgh. 

Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory. He then joined Culham Laboratory where he worked on the theory of magnetically confined plasmas as part of the European controlled fusion programme.
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Einband gebundene Ausgabe
Seitenzahl 740
Erscheinungsdatum 06.04.2011
Sprache Englisch
ISBN 978-0-387-31073-2
Reihe Information Science and Statistics
Verlag Springer
Maße (L/B/H) 26,3/18,7/4,3 cm
Gewicht 1928 g
Abbildungen mit 304 Farbabbildungen
Auflage 1st ed. 2006. Corr. 2nd printing 2011
Verkaufsrang 13.776
Buch (gebundene Ausgabe, Englisch)
Buch (gebundene Ausgabe, Englisch)
Fr. 113.90
Fr. 113.90
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inkl. gesetzl. MwSt.
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geniales buch
von einer Kundin/einem Kunden am 17.04.2009

Das Buch deckt viel Stoff von hoeheren Semestern ab und ist sicher auch fuer das PHD Studium gut. Mit den vielen Zeichnungen und Skizzen werden die Formeln sehr gut erklaert. Man findet selten ein Buch mit einer solchen Dichte an Informationen die auch noch verstaendlich erklaert sind.