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Bayesian Reasoning and Machine Learning

$81.99 (X)

  • Date Published: March 2012
  • availability: In stock
  • format: Hardback
  • isbn: 9780521518147

$ 81.99 (X)

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About the Authors
  • 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 hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year 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 problem-solving 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.

    • Consistent use of modelling encourages students to see the bigger picture while they develop hands-on experience
    • Full downloadable MATLAB toolbox, including demos, equips students to build their own models
    • Website includes figures from the book, LaTeX code for use in slides, and additional teaching material that enables instructors to easily set exercises and assignments
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    Reviews & endorsements

    "With approachable text, examples, exercises, guidelines for teachers, a MATLAB toolbox and an accompanying web site, Bayesian Reasoning and Machine Learning by David Barber provides everything needed for your machine learning course. Only students not included."
    Jaakko Hollmén, Aalto University

    "Barber has done a commendable job in presenting important concepts in probabilistic modeling and probabilistic aspects of machine learning. The chapters on graphical models form one of the clearest and most concise presentations I have seen. The book has wide coverage of probabilistic machine learning, including discrete graphical models, Markov decision processes, latent variable models, Gaussian process, stochastic and deterministic inference, among others. The material is excellent for advanced undergraduate or introductory graduate course in graphical models, or probabilistic machine learning. The exposition throughout the book uses numerous diagrams and examples, and the book comes with an extensive software toolbox - these will be immensely helpful for students and educators. It's also be a great resource for self-study for people with background knowledge in basic probability and linear algebra."
    Arindam Banerjee, University of Minnesota

    "I repeatedly get unsolicited comments from my students that the contents of this book have been very valuable in developing their understanding of machine learning. This book appeals to readers from many backgrounds, and is driven by examples of machine learning in action. Despite maintaining that level of accessibility, it does not avoid covering areas that are of practical use but often harder to explain. Neither does it shun a proper understanding of why the methods work; each chapter is a pointer to the overall probabilistic framework upon which these machine learning methods depend. My students praise this book because it is both coherent and practical, and because it makes fewer assumptions regarding the reader's statistical knowledge and confidence than many books in the field."
    Amos Storkey, University of Edinburgh

    "This book is an exciting addition to the literature on machine learning and graphical models. What makes it unique and interesting is that it provides a unified treatment of machine learning and related fields through graphical models, a framework of growing importance and popularity. Another feature of this book lies in its smooth transition from traditional artificial intelligence to modern machine learning. The book is well-written and truly pleasant to read. I believe that it will appeal to students and researchers with or without a solid mathematical background."
    Zheng-Hua Tan, Aalborg University

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    Product details

    • Date Published: March 2012
    • format: Hardback
    • isbn: 9780521518147
    • length: 735 pages
    • dimensions: 251 x 193 x 37 mm
    • weight: 1.71kg
    • contains: 287 b/w illus. 1 table 260 exercises
    • availability: In stock
  • Table of Contents

    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. Discrete-state Markov models
    24. Continuous-state Markov models
    25. Switching linear dynamical systems
    26. Distributed computation
    Part V. Approximate Inference:
    27. Sampling
    28. Deterministic approximate inference
    Appendix. Background mathematics

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    Bayesian Reasoning and Machine Learning

    David Barber

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  • Author

    David Barber, University College London
    David Barber is Reader in Information Processing in the Department of Computer Science, University College London.

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