skip to content
Machine learning : a probabilistic perspective Preview this item
ClosePreview this item

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
Database:WorldCat
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  Read more...
Getting this item's online copy... Getting this item's online copy...

Find a copy in the library

Getting this item's location and availability... Getting this item's location and availability...

WorldCat

Find it in libraries globally
Worldwide libraries own this item

Details

Genre/Form: Electronic books
Additional Physical Format: Print version:
Murphy, Kevin P., 1970-
Machine learning.
Cambridge, Mass. : MIT Press, ©2012
(DLC) 2012004558
(OCoLC)781277861
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 International Society for Bayesian Analysis DeGroot Prize for Statistical Science 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.

Abstract:

"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.
Retrieving notes about this item Retrieving notes about this item

Reviews

Editorial reviews

Publisher Synopsis

This comprehensive book should be of great interest to learners and practitioners in the field of machine learning. British Computer Society

 
User-contributed reviews

Tags

Be the first.
Confirm this request

You may have already requested this item. Please select Ok if you would like to proceed with this request anyway.

Close Window

Please sign in to WorldCat 

Don't have an account? You can easily create a free account.