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The Resource Machine learning : a probabilistic perspective, Kevin P. Murphy

Machine learning : a probabilistic perspective, Kevin P. Murphy

Label
Machine learning : a probabilistic perspective
Title
Machine learning
Title remainder
a probabilistic perspective
Statement of responsibility
Kevin P. Murphy
Creator
Subject
Language
eng
Summary
"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
Member of
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
Instantiates
Publication
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
Publication
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)

Library Locations

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      W2001 Lafferre Hall, Columbia, MO, 65211, US
      38.946102 -92.330125
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