Information Theory Inference and Learning Algorithms

Author: David J. C. MacKay
Publisher: Cambridge University Press
ISBN: 0521644445
Release Date: 2003
Genre: Computers

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.

Information Theory Inference And Learning Algorithms

Author: MACKAY
Publisher:
ISBN: 0521670519
Release Date:
Genre:

Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book 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. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art 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, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

Bayesian Reasoning and Machine Learning

Author: David Barber
Publisher: Cambridge University Press
ISBN: 9780521518147
Release Date: 2012-02-02
Genre: Computers

A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

Information Physics and Computation

Author: Marc Mézard
Publisher: Oxford University Press
ISBN: 9780198570837
Release Date: 2009-01-22
Genre: Computers

A very active field of research is emerging at the frontier of statistical physics, theoretical computer science/discrete mathematics, and coding/information theory. This book sets up a common language and pool of concepts, accessible to students and researchers from each of these fields.

Information Theory and Statistical Learning

Author: Frank Emmert-Streib
Publisher: Springer Science & Business Media
ISBN: 9780387848150
Release Date: 2008-11-14
Genre: Computers

This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts.

Machine Learning

Author: Kevin P. Murphy
Publisher: MIT Press
ISBN: 9780262018029
Release Date: 2012-08-24
Genre: Computers

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Power

Author: Alan Blackwell
Publisher: Cambridge University Press
ISBN: 1139445596
Release Date: 2006-01-12
Genre: Science

In this book, first published in 2006, seven internationally renowned writers address the theme of Power from the perspective of their own disciplines. Energy expert Mary Archer begins with an exploration of the power sources of our future. Astronomer Neil Tyson leads a tour of the orders of magnitude in the cosmos. Mathematician and inventor of the Game of Life John Conway demonstrates the power of simple ideas in mathematics. Screenwriter Maureen Thomas explains the mechanisms of narrative power in the media of film and videogames, Elisabeth Bronfen the emotional power carried by representations of life and death, and Derek Scott the power of patriotic music and the mysterious Mozart effect. Finally, celebrated parliamentarian Tony Benn critically assesses the reality of power and democracy in society.

Elements of Information Theory

Author: Thomas M. Cover
Publisher: John Wiley & Sons
ISBN: 9781118585771
Release Date: 2012-11-28
Genre: Computers

The latest edition of this classic is updated with new problem sets and material The Second Edition of this fundamental textbook maintains the book's tradition of clear, thought-provoking instruction. Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory. All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide readers with a solid understanding of the underlying theory and applications. Problem sets and a telegraphic summary at the end of each chapter further assist readers. The historical notes that follow each chapter recap the main points. The Second Edition features: * Chapters reorganized to improve teaching * 200 new problems * New material on source coding, portfolio theory, and feedback capacity * Updated references Now current and enhanced, the Second Edition of Elements of Information Theory remains the ideal textbook for upper-level undergraduate and graduate courses in electrical engineering, statistics, and telecommunications. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Information Theory

Author: JV Stone
Publisher: Sebtel Press
ISBN: 9780956372857
Release Date: 2015
Genre: Information theory

Originally developed by Claude Shannon in the 1940s, information theory laid the foundations for the digital revolution, and is now an essential tool in telecommunications, genetics, linguistics, brain sciences, and deep space communication. In this richly illustrated book, accessible examples are used to introduce information theory in terms of everyday games like ‘20 questions’ before more advanced topics are explored. Online MatLab and Python computer programs provide hands-on experience of information theory in action, and PowerPoint slides give support for teaching. Written in an informal style, with a comprehensive glossary and tutorial appendices, this text is an ideal primer for novices who wish to learn the essential principles and applications of information theory.

Pattern Recognition and Machine Learning

Author: Christopher M. Bishop
Publisher: Springer
ISBN: 1493938436
Release Date: 2016-08-23
Genre: Computers

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Understanding Machine Learning

Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 9781107057135
Release Date: 2014-05-19
Genre: Computers

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Evaluating Learning Algorithms

Author: Nathalie Japkowicz
Publisher: Cambridge University Press
ISBN: 9781139494144
Release Date: 2011-01-17
Genre: Computers

The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.