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Machine learning : a probabilistic perspective

Author: Kevin P Murphy
Publisher: Cambridge, Mass. : MIT Press, ©2012.
Series: Adaptive computation and machine learning.
Edition/Format:   eBook : Document : EnglishView all editions and formats
"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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Murphy, Kevin P., 1970-
Machine learning.
Cambridge, Mass. : MIT Press, ©2012
(DLC) 2012004558
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Kevin P Murphy
ISBN: 9780262305242 0262305240 9780262304320 0262304325
OCLC Number: 810414751
Awards: Winner of Winner, 2013 DeGroot Prize awarded by the International Society for Bayesian Analysis 2013
Description: 1 online resource (xxix, 1067 pages) : illustrations (chiefly color).
Contents: ""Contents""; ""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"" ""19 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""
Series Title: Adaptive computation and machine learning.
Responsibility: Kevin P. Murphy.


A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.  Read more...
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This comprehensive book should be of great interest to learners and practitioners in the field of machine learning.-British Computer Society

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