Information-Theoretic Methods in Data Science

Information-Theoretic Methods in Data Science

Book Description
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.


1. Introduction to Information Theory and Data Science
2. An Information-Theoretic Approach to Analog-to-Digital Compression
3. Compressed Sensing via Compression Codes
4. Information-Theoretic Bounds on Sketching
5. Sample Complexity Bounds for Dictionary Learning from Vectorand Tensor-Valued Data
6. Uncertainty Relations and Sparse Signal Recovery
7. Understanding Phase Transitions via Mutual Information and MMSE
8. Computing Choice: Learning Distributions over Permutations
9. Universal Clustering
10. Information-Theoretic Stability and Generalization
11. Information Bottleneck and Representation Learning
12. Fundamental Limits in Model Selection for Modern Data Analysis
13. Statistical Problems with Planted Structures: Information-Theoretical and Computational Limits
14. Distributed Statistical Inference with Compressed Data
15. Network Functional Compression
16. An Introductory Guide to Fano’s Inequality with Applications in Statistical Estimation

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