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Viser: Bayesian Reasoning and Machine Learning
Søgbar e-bog
Bayesian Reasoning and Machine Learning Vital Source e-bog
David Barber
(2012)
Cambridge University Press
874,00 kr.
Leveres umiddelbart efter køb
Søgbar e-bog
Bayesian Reasoning and Machine Learning Vital Source e-bog
David Barber
(2012)
Cambridge University Press
874,00 kr.
Leveres umiddelbart efter køb
Bayesian Reasoning and Machine Learning
David Barber
(2012)
Sprog: Engelsk
Cambridge University Press
849,00 kr.
Denne bog er lige nu i restordre hos udgiveren. Vi har p.t. ingen oplysninger om hvornår eller om den igen vil kunne leveres
Detaljer om varen
- 1. Udgave
- Vital Source searchable e-book (Reflowable pages)
- Udgiver: Cambridge University Press (Februar 2012)
- ISBN: 9781139636063
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.
Licens varighed:
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 5 sider kan printes ad gangen
Copy: højest 5 sider i alt kan kopieres (copy/paste)
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 5 sider kan printes ad gangen
Copy: højest 5 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- Vital Source searchable e-book (Fixed pages)
- Udgiver: Cambridge University Press (Februar 2012)
- ISBN: 9781139118729
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.
Licens varighed:
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: -1 sider kan printes ad gangen
Copy: højest -1 sider i alt kan kopieres (copy/paste)
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: -1 sider kan printes ad gangen
Copy: højest -1 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- Hardback: 735 sider
- Udgiver: Cambridge University Press (Februar 2012)
- ISBN: 9780521518147
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.
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.
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.
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