Information-Theoretic Methods in Data Science

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Miguel R. D. Rodrigues (University College London) Yonina C. Eldar (Weizmann Institute of Science, Israel)

Information-Theoretic Methods in Data Science

Miguel R. D. Rodrigues (University College London), Yonina C. Eldar (Weizmann Institute of Science, Israel)

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English

Cambridge University Press
08 April 2021

Information theory; Probability & statistics; Machine learning; Signal processing

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.

Edited by: Miguel R. D. Rodrigues (University College London), Yonina C. Eldar (Weizmann Institute of Science, Israel)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Dimensions: Height: 250mm, Width: 176mm, Spine: 34mm
Weight: 1.100kg
ISBN: 9781108427135
ISBN 10: 1108427138
Pages: 560
Publication Date: 08 April 2021
Audience: Professional and scholarly , Undergraduate
Format: Hardback
Publisher's Status: Active

Miguel R. D. Rodrigues is a Reader in Information Theory and Processing in the Department of Electronic and Electrical Engineering, University College London, and a Faculty Fellow at the Turing Institute, London. Yonina C. Eldar is a Professor in the Faculty of Mathematics and Computer Science at the Weizmann Institute of Science, a Fellow of the IEEE and Eurasip, and a member of the Israel Academy of Sciences and Humanities. She is the author of Sampling Theory (Cambridge, 2015), and co-editor of Convex Optimization in Signal Processing and Communications (Cambridge, 2009), and Compressed Sensing (Cambridge, 2012).