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. Dependent 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
13. Binary codes
14. Very good linear codes exist
15. Further exercises on information theory
16. Message passing
17. Constrained noiseless channels
18. 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
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

Information theory and inference, often taught separately, are here united in one entertaining 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. 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. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.