Information theory, inference, and learning algorithms / David J.C. MacKay.
Material type: TextPublisher: Cambridge : Cambridge University Press, 2003Description: xii, 628 pages : illustrations ; 26 cmContent type:- text
- unmediated
- volume
- 0521642981
- 9780521642989
- 003.54 MA.I 2003 23
- Q360 .M23 2003
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001.4202 RO.D 2015 Digital methods / | 001.4202 RO.D 2015 Digital methods / | 003.3 CH.M 2017 Machine learning : fundamental algorithms for supervised and unsupervised learning / | 003.54 MA.I 2003 Information theory, inference, and learning algorithms / | 003.54 MA.I 2003 Information theory, inference, and learning algorithms / | 004 / DA.P 2015 Projects in computing and information systems : | 004 SC.I 2016 Invitation to computer science / |
"Reprinted with corrections 2004, 22nd printing 2019"--Title page verso.
Includes bibliographical references (pages 613-619) and index.
1. Introduction to information theory -- 2. Probability, entropy, and inference -- 3. More about inference -- Part I. Data compression. 4. The source coding theorem -- 5. Symbol codes -- 6. Stream codes -- 7. Codes for integers -- Part II. Noisy-channel coding. 8. Correlated random variables -- 9. Communication over a noisy channel -- 10. The noisy-channel coding theorem -- 11. Error-correcting codes and real channels -- Part III. Further topics in information theory. 12. Hash codes: codes for efficient information retrieval -- 13. Binary codes -- 14. Very good linear codes exist -- 15. Further exercises on information theory -- 16. Message passing -- 17. Communication over constrained noiseless channels -- 18. An aside: crosswords and codebreaking -- 19. Why have sex? Information acquisition and evolution -- Part IV. Probabilities and inference. 20. An example inference task: clustering -- 21. Exact inference by complete enumeration -- 22. Maximum likelihood and clustering -- 23. Useful probability distributions -- 24. Exact marginalization -- 25. Exact marginalization in trellises -- 26. Exact marginalization in graphs -- 27. Laplace's method -- 28. Model comparison and Occam's razor -- 29. Monte Carlo methods -- 30. Efficient Monte Carlo methods -- 31. Ising models -- 32. Exact Monte Carlo sampling -- 33. Variational methods -- 34. Independent component analysis and latent variable modelling -- 35. Random inference topics -- 36. Decision theory -- 37. Bayesian inference and sampling theory -- Part V. Neural networks. 38. Introduction to neural networks -- 39. The single neuron as a classifier -- 40. Capacity of a single neuron -- 41. Learning as inference -- 42. Hopfield networks -- 43. Boltzmann machines -- 44. Supervised learning in multilayer networks -- 45. Gaussian processes -- 46. Deconvolution -- Part VI. Sparse graph codes. 47. Low-density parity-check codes -- 48. Convolutional codes and turbo codes -- 49. Repeat-accumulate codes -- 50. Digital fountain codes -- Part VII. Appendices. A. Notation -- B. Some physics -- C. Some mathematics.
This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks.
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