Inference and Learning from Data: Volume 3 - Learning (Hardcover, New Ed)

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Release date:	December 2022
Authors:	Ali H. Sayed
Dimensions:	255 x 180 x 40mm (L x W x T)
Format:	Hardcover
Pages:	990
Edition:	New Ed
ISBN-13:	978-1-00-921828-3
Categories:	Books > Computing & IT > Applications of computing > Signal processing Books > Computing & IT > Applications of computing > Pattern recognition Books > Reference & Interdisciplinary > Communication studies > Information theory > General Books > Professional & Technical > Electronics & communications engineering > Communications engineering / telecommunications > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	1-00-921828-X
Barcode:	9781009218283

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