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Information theory, inference, and learning algorithms / David J.C. MacKay.

By: Material type: TextTextPublisher: Cambridge : Cambridge University Press, 2003Description: xii, 628 pages : illustrations ; 26 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 0521642981
  • 9780521642989
Subject(s): DDC classification:
  • 003.54 MA.I 2003 23
LOC classification:
  • Q360 .M23 2003
Contents:
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.
Summary: 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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Books Books The Knowledge Hub Library Computing 003.54 MA.I 2003 (Browse shelf(Opens below)) Not For Loan 211920
Books Books The Knowledge Hub Library Computing 003.54 MA.I 2003 (Browse shelf(Opens below)) Available 190233

"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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