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Bayesian Reasoning and Machine Learning

    Book Name: Bayesian Reasoning and Machine Learning
    Category: Machine Learning
    Language: English
    Format: PDF
    Free Download: Available


    Bayesian Reasoning and Machine Learning

    Bayesian Reasoning and Machine Learning PDF

    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.

    Table of contents :

    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

     


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