The Resource Machine learning : a probabilistic perspective, Kevin P. Murphy
Machine learning : a probabilistic perspective, Kevin P. Murphy
Resource Information
The item Machine learning : a probabilistic perspective, Kevin P. Murphy represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 1 library branch.
Resource Information
The item Machine learning : a probabilistic perspective, Kevin P. Murphy represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 1 library branch.
 Summary
 "This textbook offers a comprehensive and selfcontained 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 pseudocode 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 modelbased 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 packagePMTK (probabilistic modeling toolkit)that is freely available online"Back cover
 Language
 eng
 Extent
 xxix, 1,071 pages
 Contents

 Probability
 Generative models for discrete data
 Gaussian models
 Bayesian statistics
 Frequentist statistics
 Linear regression
 Logistic regression
 Generalized linear models and the exponential family
 Directed graphical models (Bayes nets)
 Mixture models and the EM algorithm
 Latent linear models
 Sparse linear models
 Kernels
 Gaussian processes
 Adaptive basis function models
 Markov and hidden Markov models
 State space models
 Undirected graphical models (Markov random fields)
 Exact inference for graphical models
 Variational inference
 More variational inference
 Monte Carlo inference
 Markov chain Monte Carlo (MCMC) inference
 Clustering
 Graphical model structure learning
 Latent variable models for discrete data
 Deep learning
 Notation
 Isbn
 9780262018029
 Label
 Machine learning : a probabilistic perspective
 Title
 Machine learning
 Title remainder
 a probabilistic perspective
 Statement of responsibility
 Kevin P. Murphy
 Language
 eng
 Summary
 "This textbook offers a comprehensive and selfcontained 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 pseudocode 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 modelbased 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 packagePMTK (probabilistic modeling toolkit)that is freely available online"Back cover
 Cataloging source
 DLC
 http://library.link/vocab/creatorDate
 1970
 http://library.link/vocab/creatorName
 Murphy, Kevin P.
 Dewey number
 006.3/1
 Illustrations
 illustrations
 Index
 index present
 LC call number
 Q325.5
 LC item number
 .M87 2012
 Literary form
 non fiction
 Nature of contents
 bibliography
 Series statement
 Adaptive computation and machine learning series
 http://library.link/vocab/subjectName

 Machine learning
 Probabilities
 Label
 Machine learning : a probabilistic perspective, Kevin P. Murphy
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 volume
 Carrier category code
 nc
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents
 Probability  Generative models for discrete data  Gaussian models  Bayesian statistics  Frequentist statistics  Linear regression  Logistic regression  Generalized linear models and the exponential family  Directed graphical models (Bayes nets)  Mixture models and the EM algorithm  Latent linear models  Sparse linear models  Kernels  Gaussian processes  Adaptive basis function models  Markov and hidden Markov models  State space models  Undirected graphical models (Markov random fields)  Exact inference for graphical models  Variational inference  More variational inference  Monte Carlo inference  Markov chain Monte Carlo (MCMC) inference  Clustering  Graphical model structure learning  Latent variable models for discrete data  Deep learning  Notation
 Control code
 781277861
 Dimensions
 24 cm
 Extent
 xxix, 1,071 pages
 Isbn
 9780262018029
 Isbn Type
 (hardcover : alk. paper)
 Lccn
 2012004558
 Media category
 unmediated
 Media MARC source
 rdamedia
 Media type code
 n
 Other physical details
 illustrations (chiefly color)
 Label
 Machine learning : a probabilistic perspective, Kevin P. Murphy
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 volume
 Carrier category code
 nc
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents
 Probability  Generative models for discrete data  Gaussian models  Bayesian statistics  Frequentist statistics  Linear regression  Logistic regression  Generalized linear models and the exponential family  Directed graphical models (Bayes nets)  Mixture models and the EM algorithm  Latent linear models  Sparse linear models  Kernels  Gaussian processes  Adaptive basis function models  Markov and hidden Markov models  State space models  Undirected graphical models (Markov random fields)  Exact inference for graphical models  Variational inference  More variational inference  Monte Carlo inference  Markov chain Monte Carlo (MCMC) inference  Clustering  Graphical model structure learning  Latent variable models for discrete data  Deep learning  Notation
 Control code
 781277861
 Dimensions
 24 cm
 Extent
 xxix, 1,071 pages
 Isbn
 9780262018029
 Isbn Type
 (hardcover : alk. paper)
 Lccn
 2012004558
 Media category
 unmediated
 Media MARC source
 rdamedia
 Media type code
 n
 Other physical details
 illustrations (chiefly color)
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<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.library.missouri.edu/portal/Machinelearningaprobabilisticperspective/N55bHZ45dW4/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/portal/Machinelearningaprobabilisticperspective/N55bHZ45dW4/">Machine learning : a probabilistic perspective, Kevin P. Murphy</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>