## Bayesian Reasoning and Machine Learning

### Book Description:

Machine learning methods extract value from large 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 robotic locomotion, and their use is spreading rapidly. People who know the methods can choose rewarding jobs. This practical text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final year undergraduate and masters students with limited experience in linear algebra and calculus. Complete and coherent, it develops from basic reasoning to advanced techniques in the field of graphic models. Students learn more than a menu of techniques, develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer and theoretical, are included in each chapter. Resources for students and teachers, including a MATLAB toolbox, are available online

This hands-on introduction for undergraduates and graduates is ideal for computer scientists who are new to calculus and linear algebra. Numerous examples and exercises are provided.

Machine learning methods extract value from large 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 robotic locomotion, and their use is spreading rapidly. People who know the methods can choose rewarding jobs. This practical text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final year undergraduate and masters students with limited experience in linear algebra and calculus. Complete and coherent, it develops from basic reasoning to advanced techniques in the field of graphic models. Students learn more than a menu of techniques, develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer and theoretical, are included in each chapter. Resources for students and teachers, including a MATLAB toolbox, are available online.

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

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

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

IV. Dynamical Models: 23. Discrete-state Markov models; 24. Continuous-state Markov models; 25. Switching linear dynamical systems; 26. Distributed computation

V. Approximate Inference: 27. Sampling; 28. Deterministic approximate inference —
Appendix. Background mathematics.

Abstract: A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus. Read more…

### Bayesian reasoning and machine learning

Author(s): Barber, D

Publisher: Cambridge University Press, Year: 2018

ISBN: 9780521518147,0521518148