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.
"synopsis" may belong to another edition of this title.
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.
"About this title" may belong to another edition of this title.
FREE shipping within U.S.A.
Destination, rates & speedsSeller: BooksRun, Philadelphia, PA, U.S.A.
Hardcover. Condition: As New. New. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported. Seller Inventory # 100921828X-10-1
Quantity: 2 available
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: New. Seller Inventory # 44790675-n
Quantity: 1 available
Seller: Best Price, Torrance, CA, U.S.A.
Condition: New. SUPER FAST SHIPPING. Seller Inventory # 9781009218283
Quantity: 1 available
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # GB-9781009218283
Quantity: 2 available
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition. Seller Inventory # 44790675
Quantity: 1 available
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New. Seller Inventory # 44790675-n
Quantity: 2 available
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Condition: New. In. Seller Inventory # ria9781009218283_new
Quantity: 2 available
Seller: Grand Eagle Retail, Fairfield, OH, U.S.A.
Hardcover. Condition: new. Hardcover. 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. Written in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to learning methods. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning and engineering. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781009218283
Quantity: 1 available
Seller: Revaluation Books, Exeter, United Kingdom
Hardcover. Condition: Brand New. 990 pages. 9.80x7.20x1.69 inches. In Stock. Seller Inventory # __100921828X
Quantity: 1 available
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: As New. Unread book in perfect condition. Seller Inventory # 44790675
Quantity: 2 available