How long does it take to read Information Theory, Inference & Learning Algorithms?
Information Theory, Inference & Learning Algorithms by David J. C. MacKay takes about 11 hours 44 minutes to read at 250 words a minute.
About 11 hours 44 minutes
A week or two of reading
First published 2003. 640 pages, about 176,000 words.
24 days at 30 minutes a day
Estimate based on 275 words per page.
At other speeds
| Slow, 150 words a minute | 19 hours 33 minutes |
| Average, 250 words a minute | 11 hours 44 minutes |
| Fast, 400 words a minute | 7 hours 20 minutes |
The page count is the median across 3 editions, so your copy may differ. How we calculate.
About the book
Book Jacket: > 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. Publisher Description: > This textbook offers comprehensive coverage of Shannon's theory of information as well as the theory of neural networks and probabilistic data modelling. It includes explanations of Shannon's important source encoding theorem and noisy channel theorem as well as descriptions of practical data compression systems. Many examples and exercises make the book ideal for students to use as a class textbook, or as a resource for researchers who need to work with neural networks or state-of-the-art error-correcting codes.
Subjects: Information theory, Inference, Machine Learning, Bayesian, Aprendizado computacional, Information, Théorie de l'
