ANN: 'Information Theory, Inference and Learning Algorithms', MacKay

New from Cambridge University Press:

'Information Theory, Inference and Learning Algorithms'
by David MacKay, University of Cambridge

'An instant classic...you'll want two copies of this astonishing book, one
for the office and one for the fireside at home.' Bob McEliece, California
Institute of Technology

 'An utterly original book.' Dave Forney, MIT

Information theory and inference, often taught separately, are here united
in one textbook. These topics lie at the heart of many exciting areas of
contemporary science and engineering - communication, signal processing,
data mining, machine learning, pattern recognition, computational
neuroscience, bioinformatics and cryptography.

Hardback  0521642981
2003  640pp

For more information, including pricing please visit:
http://books.cambridge.org/0521642981.htm

If you are  a lecturer interested in obtaining an inspection copy of the
book for your course, please email: [EMAIL PROTECTED]

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 code
49. Repeat-accumulate codes
50. Digital fountain codes

Part VII. Appendices:
A. Notation
B. Some physics
C. Some mathematics
Bibliography
Index.

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