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Information Theory Inference and Learning Algorithms

Information Theory  Inference and Learning Algorithms Author David J. C. MacKay
ISBN-10 0521642981
Release 2003-09-25
Pages 628
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Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.



Information Theory Inference and Learning Algorithms

Information Theory  Inference and Learning Algorithms Author David J. C. MacKay
ISBN-10 0521644445
Release 2003
Pages 640
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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

Information Theory   Inference And Learning Algorithms Author MACKAY
ISBN-10 0521670519
Release
Pages 640
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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

Bayesian Reasoning and Machine Learning Author David Barber
ISBN-10 9780521518147
Release 2012-02-02
Pages 697
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A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.



Information Theory and Reliable Communication

Information Theory and Reliable Communication Author Robert Gallager
ISBN-10 9783709129456
Release 2014-05-04
Pages 115
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Information Theory and Reliable Communication has been writing in one form or another for most of life. You can find so many inspiration from Information Theory and Reliable Communication also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Information Theory and Reliable Communication book for free.



Entropy and Information Theory

Entropy and Information Theory Author Robert M. Gray
ISBN-10 9781475739824
Release 2013-03-14
Pages 332
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Entropy and Information Theory has been writing in one form or another for most of life. You can find so many inspiration from Entropy and Information Theory also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Entropy and Information Theory book for free.



Information Theory and Statistical Learning

Information Theory and Statistical Learning Author Frank Emmert-Streib
ISBN-10 9780387848150
Release 2008-11-14
Pages 439
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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

Machine Learning Author Kevin P. Murphy
ISBN-10 9780262018029
Release 2012-08-24
Pages 1067
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A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.



Power

Power Author Alan Blackwell
ISBN-10 1139445596
Release 2006-01-12
Pages
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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.



Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning Author Christopher M. Bishop
ISBN-10 1493938436
Release 2016-08-23
Pages 738
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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.



Elements of Information Theory

Elements of Information Theory Author Thomas M. Cover
ISBN-10 9781118585771
Release 2012-11-28
Pages 792
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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.



Statistical Mechanics of Learning

Statistical Mechanics of Learning Author A. Engel
ISBN-10 0521774799
Release 2001-03-29
Pages 329
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Artificial neural networks, learning, statistical mechanics; background material in mathematics and physics; examples and exercises; textbook/reference.



Information Theory

Information Theory Author JV Stone
ISBN-10 9780956372857
Release 2015
Pages 243
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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.



Introduction To The Theory Of Neural Computation

Introduction To The Theory Of Neural Computation Author John A. Hertz
ISBN-10 9780429979293
Release 2018-03-08
Pages 352
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Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.



Bayesian Logical Data Analysis for the Physical Sciences

Bayesian Logical Data Analysis for the Physical Sciences Author Phil Gregory
ISBN-10 9781139444286
Release 2005-04-14
Pages
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Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.



Algorithmic Graph Theory

Algorithmic Graph Theory Author Alan Gibbons
ISBN-10 0521288819
Release 1985-06-27
Pages 259
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An introduction to pure and applied graph theory with an emphasis on algorithms and their complexity.



Understanding Machine Learning

Understanding Machine Learning Author Shai Shalev-Shwartz
ISBN-10 9781107057135
Release 2014-05-19
Pages 409
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Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.