David MacKay

 
Helpful votes received on reviews: 100% (1 of 1)
 

Reviews

Top Reviewer Ranking: 586,892 - Total Helpful Votes: 1 of 1
Information Theory, Inference and Learning Algorit&hellip by David J. C. MacKay
1 of 1 people found the following review helpful
5.0 out of 5 stars Brings theory to life, Feb. 28 2004
Fantastically good value, this wide-ranging textbook covers elementary information theory, data compression, and coding theory; machine learning, Bayesian inference, Monte Carlo methods; and state of the art error-correcting coding methods, including low-density parity-check codes, turbo codes, and digital fountain codes. Theory and practical examples are covered side by side. Hundreds of exercises are included, many with worked solutions.
Three things are distinctive about this book.
First, it emphasizes the connections between information theory and machine learning - for example data compression and Bayesian data modelling are two sides of the same coin.
Second, since… Read more
Information Theory, Inference and Learning Algorit&hellip by David J. C. MacKay
Fantastically good value, this wide-ranging textbook covers elementary information theory, data compression, and coding theory; machine learning, Bayesian inference, Monte Carlo methods; and state of the art error-correcting coding methods, including low-density parity-check codes, turbo codes, and digital fountain codes. Theory and practical examples are covered side by side. Hundreds of exercises are included, many with worked solutions.
Two things are distinctive about this book.
First, it emphasizes the connections between information theory and machine learning - for example data compression and Bayesian data modelling are two sides of the same coin.
Second, since 1993,… Read more