The Resource Bayesian reasoning and machine learning, David Barber
Bayesian reasoning and machine learning, David Barber
Resource Information
The item Bayesian reasoning and machine learning, David Barber represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Liverpool.This item is available to borrow from 1 library branch.
Resource Information
The item Bayesian reasoning and machine learning, David Barber represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Liverpool.
This item is available to borrow from 1 library branch.
 Summary

 "Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This handson text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for finalyear undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problemsolving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online" Provided by publisher
 "Vast amounts of data present amajor challenge to all thoseworking in computer science, and its many related fields, who need to process and extract value from such data. Machine learning technology is already used to help with this task in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis and robot locomotion. As its usage becomes more widespread, no student should be without the skills taught in this book. Designed for finalyear undergraduate and graduate students, this gentle introduction is ideally suited to readers without a solid background in linear algebra and calculus. It covers everything from basic reasoning to advanced techniques in machine learning, and rucially enables students to construct their own models for realworld problems by teaching them what lies behind the methods. Numerous examples and exercises are included in the text. Comprehensive resources for students and instructors are available online" Provided by publisher
 Language
 eng
 Extent
 xxiv, 697 p.
 Contents

 Machine generated contents note: Preface; Part I. Inference in Probabilistic Models: 1. Probabilistic reasoning; 2. Basic graph concepts; 3. Belief networks; 4. Graphical models; 5. Efficient inference in trees; 6. The junction tree algorithm; 7. Making decisions; Part II. Learning in Probabilistic Models: 8. Statistics for machine learning; 9. Learning as inference; 10. Naive Bayes; 11. Learning with hidden variables; 12. Bayesian model selection; Part III. Machine Learning: 13. Machine learning concepts; 14. Nearest neighbour classification; 15. Unsupervised linear dimension reduction; 16. Supervised linear dimension reduction; 17. Linear models; 18. Bayesian linear models; 19. Gaussian processes; 20. Mixture models; 21. Latent linear models; 22. Latent ability models; Part IV. Dynamical Models: 23. Discretestate Markov models; 24. Continuousstate Markov models; 25. Switching linear dynamical systems; 26. Distributed computation; Part V. Approximate Inference: 27. Sampling; 28. Deterministic approximate inference; Appendix. Background mathematics; Bibliography; Index
 Isbn
 9780521518147
 Label
 Bayesian reasoning and machine learning
 Title
 Bayesian reasoning and machine learning
 Statement of responsibility
 David Barber
 Language
 eng
 Summary

 "Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This handson text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for finalyear undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problemsolving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online" Provided by publisher
 "Vast amounts of data present amajor challenge to all thoseworking in computer science, and its many related fields, who need to process and extract value from such data. Machine learning technology is already used to help with this task in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis and robot locomotion. As its usage becomes more widespread, no student should be without the skills taught in this book. Designed for finalyear undergraduate and graduate students, this gentle introduction is ideally suited to readers without a solid background in linear algebra and calculus. It covers everything from basic reasoning to advanced techniques in machine learning, and rucially enables students to construct their own models for realworld problems by teaching them what lies behind the methods. Numerous examples and exercises are included in the text. Comprehensive resources for students and instructors are available online" Provided by publisher
 Cataloging source
 DLC
 http://library.link/vocab/creatorDate
 1968
 http://library.link/vocab/creatorName
 Barber, David
 Index
 index present
 Literary form
 non fiction
 Nature of contents
 bibliography
 http://library.link/vocab/subjectName

 Machine learning
 Bayesian statistical decision theory
 Label
 Bayesian reasoning and machine learning, David Barber
 Bibliography note
 Includes bibliographical references and index
 Contents
 Machine generated contents note: Preface; Part I. Inference in Probabilistic Models: 1. Probabilistic reasoning; 2. Basic graph concepts; 3. Belief networks; 4. Graphical models; 5. Efficient inference in trees; 6. The junction tree algorithm; 7. Making decisions; Part II. Learning in Probabilistic Models: 8. Statistics for machine learning; 9. Learning as inference; 10. Naive Bayes; 11. Learning with hidden variables; 12. Bayesian model selection; Part III. Machine Learning: 13. Machine learning concepts; 14. Nearest neighbour classification; 15. Unsupervised linear dimension reduction; 16. Supervised linear dimension reduction; 17. Linear models; 18. Bayesian linear models; 19. Gaussian processes; 20. Mixture models; 21. Latent linear models; 22. Latent ability models; Part IV. Dynamical Models: 23. Discretestate Markov models; 24. Continuousstate Markov models; 25. Switching linear dynamical systems; 26. Distributed computation; Part V. Approximate Inference: 27. Sampling; 28. Deterministic approximate inference; Appendix. Background mathematics; Bibliography; Index
 Control code
 ocn701022184
 Dimensions
 26 cm.
 Extent
 xxiv, 697 p.
 Isbn
 9780521518147
 Lccn
 2011035553
 Other physical details
 ill.
 Label
 Bayesian reasoning and machine learning, David Barber
 Bibliography note
 Includes bibliographical references and index
 Contents
 Machine generated contents note: Preface; Part I. Inference in Probabilistic Models: 1. Probabilistic reasoning; 2. Basic graph concepts; 3. Belief networks; 4. Graphical models; 5. Efficient inference in trees; 6. The junction tree algorithm; 7. Making decisions; Part II. Learning in Probabilistic Models: 8. Statistics for machine learning; 9. Learning as inference; 10. Naive Bayes; 11. Learning with hidden variables; 12. Bayesian model selection; Part III. Machine Learning: 13. Machine learning concepts; 14. Nearest neighbour classification; 15. Unsupervised linear dimension reduction; 16. Supervised linear dimension reduction; 17. Linear models; 18. Bayesian linear models; 19. Gaussian processes; 20. Mixture models; 21. Latent linear models; 22. Latent ability models; Part IV. Dynamical Models: 23. Discretestate Markov models; 24. Continuousstate Markov models; 25. Switching linear dynamical systems; 26. Distributed computation; Part V. Approximate Inference: 27. Sampling; 28. Deterministic approximate inference; Appendix. Background mathematics; Bibliography; Index
 Control code
 ocn701022184
 Dimensions
 26 cm.
 Extent
 xxiv, 697 p.
 Isbn
 9780521518147
 Lccn
 2011035553
 Other physical details
 ill.
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<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.liverpool.ac.uk/portal/BayesianreasoningandmachinelearningDavid/ip0Ej6HStB8/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.liverpool.ac.uk/portal/BayesianreasoningandmachinelearningDavid/ip0Ej6HStB8/">Bayesian reasoning and machine learning, David Barber</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.liverpool.ac.uk/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.liverpool.ac.uk/">University of Liverpool</a></span></span></span></span></div>