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. 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; Bibliography; Index.
A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.
David Barber is Reader in Information Processing in the Department of Computer Science, University College London.
'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, Denmark
'With approachable text, examples, exercises, guidelines for
teachers, a MATLAB toolbox and an accompanying website, Bayesian
Reasoning and Machine Learning by David Barber provides everything
needed for your machine learning course. Only students not
included.' Jaakko Hollmén, Aalto University
'The chapters on graphical models form one of the clearest and most
concise presentations I have seen … The exposition throughout 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 a great resource for self-study.'
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 … 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
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