Bayesian Reasoning and Machine Learning > 데이터마이닝

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.