Discover core topics in inference and learning with this extraordinary three-volume set.
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Ali H. Sayed is Professor and Dean of Engineering at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He has also served as Distinguished Professor and Chairman of Electrical Engineering at the University of California, Los Angeles, USA, and as President of the IEEE Signal Processing Society. He is a member of the US National Academy of Engineering (NAE) and The World Academy of Sciences (TWAS), and a recipient of the 2022 IEEE Fourier Award and the 2020 IEEE Norbert Wiener Society Award. He is a Fellow of the IEEE.
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Anbieter: Prior Books Ltd, Cheltenham, Vereinigtes Königreich
Hardcover. Zustand: Like New. First Edition. A complete three volume set: all volumes firm and square, bright and clean with strong joints and sharp corners. Just showing a few very mild bumps and rubs. Hence a non-text page is stamped 'damaged'. Despite such these books are crisp, fresh and tight with no defects. Thus a nearly new set of books that looks and feels unread now offered for sale at a very reasonable price. Artikel-Nr. 202868
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Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Zustand: New. In. Artikel-Nr. ria9781009218108_new
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Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich
PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000. Artikel-Nr. GB-9781009218108
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Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
3 Hardback books. Zustand: Brand New. 1st edition. 3370 pages. 11.26x8.35x6.38 inches. In Stock. Artikel-Nr. __1009218107
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Anbieter: Kennys Bookstore, Olney, MD, USA
Zustand: New. 2022. New. hardcover. . . . . . Books ship from the US and Ireland. Artikel-Nr. V9781009218108
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Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Neuware - This extraordinary three-volume work provides an accessible, comprehensive introduction to mathematical and statistical techniques for data-driven learning and inference. Ideal for early-career researchers and graduate students across signal processing, machine learning, statistics and data science. Artikel-Nr. 9781009218108
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