Inference Learning Data Volume by Sayed Ali (32 results)

Hardcover. Condition: Very Good. New. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Hardcover. Condition: Good. Clean pages. Some spine separation. Boards unmarked.

hardcover. Condition: Very Good. In stock ready to dispatch from the UK.

Hardback. Ali H. Sayed (Ecole Polytechnique Federale de Lausanne), Cambridge University Press. 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 first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, 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, statistical analysis, data science and inference. Hardback.…

Hardcover. Condition: New. New. The item is brand new, never used or read. It's in perfect condition and may include supplements and/or access codes or come shrink-wrapped.

Hardcover. Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: New. In English.

Condition: New. 2022. New. Hardcover. . . . . .

Condition: New. In English.

Condition: New.

Condition: New.

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.…

Condition: New. 2022. New. Hardcover. . . . . . Books ship from the US and Ireland.

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 second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, 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, statistical analysis, data science and inference. Written in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to techniques for inferring unknown variables and quantities. 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.…

Condition: As New. Unread book in perfect condition.

Hardcover. Condition: Brand New. 990 pages. 9.80x7.20x1.69 inches. In Stock.

Hardback. Condition: New. 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.…

hardcover. Condition: New. Special order direct from the distributor.

Condition: New. 2022. New. Hardcover. . . . . .

Condition: New.

Gebunden. Condition: New.

Condition: New.

Condition: New. 2022. New. Hardcover. . . . . . Books ship from the US and Ireland.

Hardcover. Condition: 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.…

Buch. Condition: Neu. Inference and Learning from Data: Volume 3 | Learning | Ali H. Sayed | Buch | Gebunden | Englisch | 2022 | Cambridge University Press | EAN 9781009218283 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Hardcover. Condition: Brand New. 990 pages. 9.80x7.20x1.69 inches. In Stock.

Buch. Condition: Neu. Neuware - 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.…

Hardcover. Condition: Brand New. 1010 pages. 9.50x7.00x1.75 inches. In Stock.

Hardcover. Condition: Brand New. 1070 pages. 9.88x7.24x1.77 inches. In Stock.