Information Theory, Inference & Learning Algorithms
David J.C. MacKay · 2003 · 640 pages
As an Amazon Associate we earn from qualifying purchases. Some book links are affiliate links; you pay the same price and we may earn a small commission.
About this 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.
Appears in these reading paths
Learn information theory: the best books to read in order
Related reading guides
Reader reviews
Ratings and notes from readers — tagged with how deep into the subject they were.
Loading reviews…