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Information Theory, Inference And Learning Algorithms
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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 Sparsegraph Codes For Errorcorrection. A Toolbox Of Inference Techniques, Including Messagepassing 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 Errorcorrecting Codes, Including Lowdensity Paritycheck Codes, Turbo Codes, And Digital Fountain Codes The Twentyfirst 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 Selflearning 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.
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This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: What is the page count of this book? A: This book contains six hundred forty-two pages. It's a comprehensive resource covering information theory, inference, and learning algorithms.
- Q: What is the binding type of this textbook? A: This textbook is available in paperback binding. This makes it lightweight and easy to handle for students and professionals alike.
- Q: Who is the author of this book? A: The author of this book is David J. C. MacKay. He is well-known for his contributions to information theory and machine learning.
- Q: What topics does this book cover? A: This book covers information theory, inference, and learning algorithms. It also explores applications in various fields like communication and data mining.
- Q: What is the size of this book? A: The dimensions of this book are seven point zero one inches in length, one point four six inches in width, and ten inches in height. It is a standard textbook size.
- Q: Is this book suitable for beginners? A: Yes, this textbook is suitable for beginners and advanced learners. It is designed for self-learning and can be used in undergraduate or graduate courses.
- Q: Can this book be used for self-study? A: Yes, this book is ideal for self-learning. It is filled with worked examples and exercises to enhance understanding of complex concepts.
- Q: What kind of exercises are included in the book? A: The book includes over four hundred exercises, some with detailed solutions. These exercises help reinforce the material covered in each chapter.
- Q: What should I do to care for this book? A: To care for this book, keep it in a dry place and avoid exposure to direct sunlight. This will help maintain its condition over time.
- Q: Is this book appropriate for professionals in technical fields? A: Yes, this textbook serves as an entry point for professionals in fields like computational biology and financial engineering. It addresses key concepts relevant to various industries.
- Q: What if the book arrives damaged? A: If the book arrives damaged, you should contact the seller for a return or exchange. Most sellers offer a return policy for damaged items.
- Q: What is the publishing brand of this book? A: The book is published by Cambridge University Press. This publisher is known for high-quality academic texts.
- Q: Does this book include illustrations? A: Yes, this textbook is richly illustrated. The illustrations support the text and help clarify complex topics.
- Q: Are there any entertaining elements in this book? A: Yes, the book includes interludes on topics like crosswords and evolution. These elements provide a light-hearted break from the technical content.
- Q: Is there a warranty for this book? A: Typically, books do not come with a warranty. However, check with the seller for their specific return policies.
- Q: What are the main applications discussed in the book? A: The book discusses applications in communication systems, data mining, and machine learning, among others. It showcases practical implementations of theoretical concepts.