Information-theoretic methods in data science / edited by Miguel R. D. Rodrigues, Yonina C. Eldar.

Format:

Book

Contributor:

Rodrigues, Miguel R. D. (Miguel Raul Dias), editor.

Eldar, Yonina C., editor.

Language:

English

Subjects (All):

Data mining.

Information theory.

Machine learning.

Physical Description:

1 online resource (xxi, 538 pages) : digital, PDF file(s)

Place of Publication:

Cambridge : Cambridge University Press, 2021.

System Details:

text file

PDF

Summary:

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.

Notes:

Title from publisher's bibliographic system (viewed on 26 Mar 2021).

Other Format:

Print version:

ISBN:

9781108616799

Access Restriction:

Restricted for use by site license.