Download PDF Pattern Recognition and Machine Learning (Information Science and Statistics), by Christopher M. Bishop
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Pattern Recognition and Machine Learning (Information Science and Statistics), by Christopher M. Bishop
Download PDF Pattern Recognition and Machine Learning (Information Science and Statistics), by Christopher M. Bishop
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Pressestimmen
From the reviews: "This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group." John Maindonald for the Journal of Statistical Software "In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of ‘pattern recognition’ or ‘machine learning’. … This book will serve as an excellent reference. … With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop’s book is a useful introduction … and a valuable reference for the principle techniques used in these fields." (Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007) "This book appears in the Information Science and Statistics Series commissioned by the publishers. … The book appears to have been designed for course teaching, but obviously contains material that readers interested in self-study can use. It is certainly structured for easy use. … For course teachers there is ample backing which includes some 400 exercises. … it does contain important material which can be easily followed without the reader being confined to a pre-determined course of study." (W. R. Howard, Kybernetes, Vol. 36 (2), 2007) "Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. Aimed at advanced undergraduates and first-year graduate students, as well as researchers and practitioners, the book assumes knowledge of multivariate calculus and linear algebra … . Summing Up: Highly recommended. Upper-division undergraduates through professionals." (C. Tappert, CHOICE, Vol. 44 (9), May, 2007) "The book is structured into 14 main parts and 5 appendices. … The book is aimed at PhD students, researchers and practitioners. It is well-suited for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bio-informatics. Extensive support is provided for course instructors, including more than 400 exercises, lecture slides and a great deal of additional material available at the book’s web site … ." (Ingmar Randvee, Zentralblatt MATH, Vol. 1107 (9), 2007) "This new textbook by C. M. Bishop is a brilliant extension of his former book ‘Neural Networks for Pattern Recognition’. It is written for graduate students or scientists doing interdisciplinary work in related fields. … In summary, this textbook is an excellent introduction to classical pattern recognition and machine learning (in the sense of parameter estimation). A large number of very instructive illustrations adds to this value." (H. G. Feichtinger, Monatshefte für Mathematik, Vol. 151 (3), 2007) "Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. … Pattern Recognition and Machine Learning provides excellent intuitive descriptions and appropriate-level technical details on modern pattern recognition and machine learning. It can be used to teach a course or for self-study, as well as for a reference. … I strongly recommend it for the intended audience and note that Neal (2007) also has given this text a strong review to complement its strong sales record." (Thomas Burr, Journal of the American Statistical Association, Vol. 103 (482), June, 2008) "This accessible monograph seeks to provide a comprehensive introduction to the fields of pattern recognition and machine learning. It presents a unified treatment of well-known statistical pattern recognition techniques. … The book can be used by advanced undergraduates and graduate students … . The illustrative examples and exercises proposed at the end of each chapter are welcome … . The book, which provides several new views, developments and results, is appropriate for both researchers and students who work in machine learning … ." (L. State, ACM Computing Reviews, October, 2008) "Chris Bishop’s … technical exposition that is at once lucid and mathematically rigorous. … In more than 700 pages of clear, copiously illustrated text, he develops a common statistical framework that encompasses … machine learning. … it is a textbook, with a wide range of exercises, instructions to tutors on where to go for full solutions, and the color illustrations that have become obligatory in undergraduate texts. … its clarity and comprehensiveness will make it a favorite desktop companion for practicing data analysts." (H. Van Dyke Parunak, ACM Computing Reviews, Vol. 49 (3), March, 2008)
Synopsis
The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, 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 techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.This completely new textbook reflects 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 well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed.Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information. Coming soon: for students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text); for instructors, worked solutions to remaining exercises from the Springer web site; lecture slides to accompany each chapter; and, data sets available for download.
Alle Produktbeschreibungen
Produktinformation
Gebundene Ausgabe: 738 Seiten
Verlag: Springer; Auflage: 1st ed. 2006. Corr. 2nd printing 2011 (2007)
Sprache: Englisch
ISBN-10: 0387310738
ISBN-13: 978-0387310732
Größe und/oder Gewicht:
18,5 x 4,3 x 23,6 cm
Durchschnittliche Kundenbewertung:
4.2 von 5 Sternen
16 Kundenrezensionen
Amazon Bestseller-Rang:
Nr. 2.305 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
Das Buch von Bishop ist ein Lehrbuch wie man es nicht so oft antrifft. Das Ziel, Musterkernnungsalgorithmen ist nur ein Aufhänger um in geschlossener Form alle Grundlagen von Wahrscheinlichkeitsrechnung, Statistik, Regressions und Klassifikationsrechnung und algorithmen Maschinellen Lernens vorzustellen. Dabei bleiben die notwendigen Vorkenntnisse auf basiskenntnisse in Linearer Algebra (Vektor- und Matrizenrechnung) und an wenigen Stellen Differentialrechnung beschränkt.Jede Thematik wird umfassend motiviert, sodass nicht wie in anderen Büchern oft das Ziel einer Rechnung vor lauter geschwurbel untergeht.Bishop ist der Bayesianischen Statistik verpflichtet. Für den Leser ist das ein großer Gewinn, der Bayesianische Ansatz ist in der Darstellung Bishops überaus verständlich und erleichtert meiner Meinung nach Insbesondere das Verständnis der Ansätze sehr. Als Konsequenz des Bayesianischen Ansatzes sind manche Rechnungen/Herleitungen durchaus etwas länger als in frequentistischer Literatur und auch oft anspruchsvoller. Am Ende bleibt aber das Gefühl die Zusammenhänge besser verstanden zu haben.An dieser Stelle möchte ich auch Hervorheben, dass das Buch viele sehr anschauliche Grafiken und Schaubilder hat. Einfach super verständlich dargestellt und visualisiert.
Target audience: Graduate students and researchers relatively new to the field of Bayesian learning.(+)Clearly written.High-quality print (figure quality is much higher than that of your average textbook).Fresh approach to HMMs and the Kalman Filter. Yes, the Kalman filter / smoother equations make much more sense when derived from a graphical model.A quick Google search will yield some accompanying lecture videos from the author (on graphical models and sequential learning).Solutions for the "www"-marked exercises are available from the author's webpage.(neutral)Formula-heavy so not for the faint of heart.(-)Little emphasis on computational / implementation aspects. There is no "official" (author's) code for the algorithms discussed in the book - however, there are some good-quality 3rd party implementations available on the web.Most of the exercises simply fill-in the missing steps in the algebra derivations. There are no coding exercises.Some of the data sets used in the book don't seem to be available anymore (at least not at the URL given in the book).Highly recommended. Best when used in conjunction with the 3rd party (MATLAB /Python) codes available on the web.
Als Kindleversion leider nicht brauchbar! Die Formeln sind nicht lesbar! Unusable in the kindle version: the equations are totally garbled!
ich betitle es als "insightful". Noch nie habe ich in einem Selbststudium so viel gelernt. Die Erklärungen sind gut und man kann sich an den Übungsaufgaben austoben. Ab und an bleibt zwar unklar, wie eine Likelihoodfunktion hergeleitet wird, aber das tut dem Buch keinen Abbruch.
Gut strukturiert mit gutem layout. Inhaltlich wie erwartet und angenehm zu lesen. Definitiv eines der besseren Lehrbücher, die ich benutzt habe.
One of the best books i read about the basics of machine learning. Good to combine with Murphy: machine learning a probabilistic view
Really good book in a really good condition. I highly recommend this book for getting to know better machine learning theory.
Beschäftige mich seit 20 Jahren mit KI (mal mehr mal weniger) und habe meine Diplomarbeit über selbstlernende Karten geschrieben. Ich wollte ein Update mit Überblick über neue Verfahren (Deep Learning und stochastische Modelle). Was ich bisher gelesen habe ist sehr gut
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