Information Theory, Inference and Learning Algorithms

A textbook on information, communication, and coding for a new generation of students, and an entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

**Tag(s):**
Information Theory

**Publication date**: 01 Mar 2005

**ISBN-10**:
0521642981

**ISBN-13**:
n/a

**Paperback**:
550 pages

**Views**: 26,420

Information Theory, Inference and Learning Algorithms

A textbook on information, communication, and coding for a new generation of students, and an entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

Terms and Conditions:

Book excerpts:

Information theory and inference, often taught separately, are here united in one textbook. These topics lie at the heart of many 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.

Intended Audience:

This book is aimed at senior undergraduates and graduate students in Engineering, Science, Mathematics, and Computing. It expects familiarity with calculus, probability theory, and linear algebra as taught in a first or second year undergraduate course on mathematics for scientists and engineers.

Review:

Amazon.com

:smile: "If you want to know what's presently going on in the field of coding theory with solid technical foundation, this is the book."

:smile: "For a course I help teach, the intoductions to probability theory and information theory save a lot of work."

David J. C. MacKay wrote:Now the book is published, these files will remain viewable on this website. The same copyright rules will apply to the online copy of the book as apply to normal books. [e.g., copying the whole book onto paper is not permitted.]

Book excerpts:

Information theory and inference, often taught separately, are here united in one textbook. These topics lie at the heart of many 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.

Intended Audience:

This book is aimed at senior undergraduates and graduate students in Engineering, Science, Mathematics, and Computing. It expects familiarity with calculus, probability theory, and linear algebra as taught in a first or second year undergraduate course on mathematics for scientists and engineers.

Review:

Amazon.com

:smile: "If you want to know what's presently going on in the field of coding theory with solid technical foundation, this is the book."

:smile: "For a course I help teach, the intoductions to probability theory and information theory save a lot of work."

Tweet

About The Author(s)

David J. C. MacKay is Regius Professor of Engineering at Cambridge University Engineering Department. He works on Bayesian probability theory and its application to inference problems.

Book Categories

Computer Science
Introduction to Computer Science
Introduction to Computer Programming
Algorithms and Data Structures
Artificial Intelligence
Computer Vision
Machine Learning
Neural Networks
Game Development and Multimedia
Data Communication and Networks
Coding Theory
Computer Security
Information Security
Cryptography
Information Theory
Computer Organization and Architecture
Operating Systems
Image Processing
Parallel Computing
Concurrent Programming
Relational Database
Document-oriented Database
Data Mining
Big Data
Data Science
Digital Libraries
Compiler Design and Construction
Functional Programming
Logic Programming
Object Oriented Programming
Formal Methods
Software Engineering
Agile Software Development
Information Systems
Geographic Information System (GIS)

Mathematics
Mathematics
Algebra
Abstract Algebra
Linear Algebra
Number Theory
Numerical Methods
Precalculus
Calculus
Differential Equations
Category Theory
Proofs
Discrete Mathematics
Theory of Computation
Graph Theory
Real Analysis
Complex Analysis
Probability
Statistics
Game Theory
Queueing Theory
Operations Research
Computer Aided Mathematics

Supporting Fields
Web Design and Development
Mobile App Design and Development
System Administration
Cloud Computing
Electric Circuits
Embedded System
Signal Processing
Integration and Automation
Network Science
Project Management

Operating System
Programming/Scripting
Ada
Assembly
C / C++
Common Lisp
Forth
Java
JavaScript
Lua
Rexx
Microsoft .NET
Perl
PHP
R
Python
Rebol
Ruby
Scheme
Tcl/Tk

Miscellaneous
Sponsors