Information-Theoretic Methods in Data Science (Hardcover)

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.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Release date:	April 2021
Editors:	Miguel R. D. Rodrigues • Yonina C. Eldar
Dimensions:	250 x 176 x 34mm (L x W x T)
Format:	Hardcover
Pages:	560
ISBN-13:	978-1-108-42713-5
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > Applications of computing > Signal processing Books > Reference & Interdisciplinary > Communication studies > Information theory > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	1-108-42713-8
Barcode:	9781108427135

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