Information-Theoretic Methods in Data Science
eBook - PDF

Information-Theoretic Methods in Data Science

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Information-Theoretic Methods in Data Science

About this book

Learn about the state-of-the-art at the interface between information theory and data science with this first unified treatment of the subject. Written by leading experts in a clear, tutorial style, and using consistent notation and definitions throughout, it shows how information-theoretic methods are being used in data acquisition, data representation, data analysis, and statistics and machine learning. Coverage is broad, with chapters on signal acquisition, data compression, compressive sensing, data communication, representation learning, emerging topics in statistics, and much more. Each chapter includes a topic overview, definition of the key problems, emerging and open problems, and an extensive reference list, allowing readers to develop in-depth knowledge and understanding. Providing a thorough survey of the current research area and cutting-edge trends, this is essential reading for graduate students and researchers working in information theory, signal processing, machine learning, and statistics.

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Yes, you can access Information-Theoretic Methods in Data Science by Miguel R. D. Rodrigues,Yonina C. Eldar in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Signals & Signal Processing. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half-title page
  3. Reviews
  4. Title page
  5. Copyright page
  6. Dedication
  7. Contents
  8. Preface
  9. Notation
  10. List of Contributors
  11. 1 Introduction to Information Theory and Data Science
  12. 2 An Information-Theoretic Approach to Analog-to-Digital Compression
  13. 3 Compressed Sensing via Compression Codes
  14. 4 Information-Theoretic Bounds on Sketching
  15. 5 Sample Complexity Bounds for Dictionary Learning from Vector- and Tensor-Valued Data
  16. 6 Uncertainty Relations and Sparse Signal Recovery
  17. 7 Understanding Phase Transitions via Mutual Information and MMSE
  18. 8 Computing Choice: Learning Distributions over Permutations
  19. 9 Universal Clustering
  20. 10 Information-Theoretic Stability and Generalization
  21. 11 Information Bottleneck and Representation Learning
  22. 12 Fundamental Limits in Model Selection for Modern Data Analysis
  23. 13 Statistical Problems with Planted Structures: Information-Theoretical and Computational Limits
  24. 14 Distributed Statistical Inference with Compressed Data
  25. 15 Network Functional Compression
  26. 16 An Introductory Guide to Fano’s Inequality with Applications in Statistical Estimation
  27. Index