MARC record from Internet Archive

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008 120919s2012 maua ob 001 0 eng d
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020 $z0262018020$q(hardcover ;$qalk. paper)
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050 4 $aQ325.5$b.M87 2012
072 7 $aCOM$x005030$2bisacsh
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080 00 $a378:519.22
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049 $aZCUA
100 1 $aMurphy, Kevin P.,$d1970-$eauthor.
245 10 $aMachine learning :$ba probabilistic perspective /$cKevin P. Murphy.
260 $aCambridge, Mass. :$bMIT Press,$c©2012.
300 $a1 online resource (xxix, 1067 pages) :$billustrations (chiefly color)
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 1 $aAdaptive computation and machine learning series
504 $aIncludes bibliographical references and indexes.
520 $a"This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online"--Back cover.
505 0 $aContents -- Preface -- 1 Introduction -- 2 Probability -- 3 Generative Models for Discrete Data -- 4 Gaussian Models -- 5 Bayesian Statistics -- 6 Frequentist Statistics -- 7 Linear Regression -- 8 Logistic Regression -- 9 Generalized Linear Models and the Exponential Family -- 10 Directed Graphical Models (Bayes Nets) -- 11 Mixture Models and the EM Algorithm -- 12 Latent Linear Models -- 13 Sparse Linear Models -- 14 Kernels -- 15 Gaussian Processes -- 16 Adaptive Basis Function Models -- 17 Markov and Hidden Markov Models -- 18 State Space Models
505 8 $a19 Undirected Graphical Models (Markov Random Fields)20 Exact Inference for Graphical Models -- 21 Variational Inference -- 22 More Variational Inference -- 23 Monte Carlo Inference -- 24 Markov Chain Monte Carlo (MCMC) Inference -- 25 Clustering -- 26 Graphical Model Structure Learning -- 27 Latent Variable Models for Discrete Data -- 28 Deep Learning -- Notation -- Bibliography -- Index to Code -- Index to Keywords
588 0 $aOnline resource; title from PDF title page (JSTOR, viewed October 20, 2016).
546 $aEnglish.
506 $aUnlimited Users and Download Restrictions may Apply, ProQuest Unlimited User Licence. Available using University of Exeter Username and Password.$5GB-UKExU
650 0 $aMachine learning.
650 0 $aProbabilities.
650 2 $aMachine Learning
650 2 $aProbability
650 6 $aApprentissage automatique.
650 6 $aProbabilités.
650 7 $aprobability.$2aat
650 7 $aCOMPUTERS$xEnterprise Applications$xBusiness Intelligence Tools.$2bisacsh
650 7 $aCOMPUTERS$xIntelligence (AI) & Semantics.$2bisacsh
650 7 $aMachine learning.$2fast$0(OCoLC)fst01004795
650 7 $aProbabilities.$2fast$0(OCoLC)fst01077737
650 17 $aMachine-learning.$2gtt
655 4 $aElectronic books.
776 08 $iPrint version:$aMurphy, Kevin P., 1970-$tMachine learning.$dCambridge, Mass. : MIT Press, ©2012$z0262018020$w(DLC) 2012004558$w(OCoLC)781277861
830 0 $aAdaptive computation and machine learning.
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio10509129$zAll EBSCO eBooks
852 8 $blweb$hEBOOKS