Information-Theoretic Methods in Data Science

Miguel R. D. Rodrigues

ISBN 10: 1108427138 / ISBN 13: 9781108427135

Published by Cambridge University Press, 2020

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9781108427135

Bibliographic Details

• Title: Information-Theoretic Methods in Data Science
• Publisher: Cambridge University Press
• Publication Date: 2020
• Language: English
• ISBN 10: 1108427138
• ISBN 13: 9781108427135
• Binding: Buch
• Condition: Neu
• Dimensions: 250W x 175H x 35D millimeters 250 millimeters width by 175 millimeters height by 35 millimeters depth
• Weight: 1137 grams

About this title

Synopsis

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.

About the Authors

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).