Information-Theoretic Methods in Data Science
$304.54
- Hardcover
560 pages
- Release Date
8 April 2021
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 se…
Book Details
| ISBN-13: | 9781108427135 |
|---|---|
| ISBN-10: | 1108427138 |
| Author: | Miguel R.D. Rodrigues, Yonina C. Eldar |
| Publisher: | Cambridge University Press |
| Imprint: | Cambridge University Press |
| Format: | Hardcover |
| Number of Pages: | 560 |
| Release Date: | 8 April 2021 |
| Weight: | 1.10kg |
| Dimensions: | 250mm x 176mm x 34mm |
About The Author
Miguel R.D. Rodrigues
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).
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