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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.Read more
- 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
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 UniversitySee more reviews
"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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- 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:
28. Deterministic approximate inference
Appendix. Background mathematics
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