Language: English
Published by Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: Friends of the Multnomah County Library, Portland, OR, U.S.A.
Hardcover. Condition: Good. Clean pages. Some spine separation. Boards unmarked.
Language: English
Published by Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New.
Language: English
Published by Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition.
Language: English
Published by Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: Ria Christie Collections, Uxbridge, United Kingdom
£ 83.97
Quantity: Over 20 available
Add to basketCondition: New. In.
Language: English
Published by Cambridge University Press 2022-12-22, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: Chiron Media, Wallingford, United Kingdom
Hardcover. Condition: New.
Language: English
Published by Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: New.
Language: English
Published by Cambridge University Press, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Condition: New. 2022. New. Hardcover. . . . . .
Language: English
Published by Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: As New. Unread book in perfect condition.
Language: English
Published by Cambridge University Press, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: Revaluation Books, Exeter, United Kingdom
Hardcover. Condition: Brand New. 1070 pages. 9.88x7.24x1.77 inches. In Stock.
Language: English
Published by Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: Kennys Bookstore, Olney, MD, U.S.A.
Condition: New. 2022. New. Hardcover. . . . . . Books ship from the US and Ireland.
Language: English
Published by Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. New edition niversity Press NO-PA16APR2015-KAP.
Language: English
Published by Cambridge University Press, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: moluna, Greven, Germany
Condition: New.
Language: English
Published by Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: AHA-BUCH GmbH, Einbeck, Germany
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
Language: English
Published by Cambridge University Press, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: Revaluation Books, Exeter, United Kingdom
Hardcover. Condition: Brand New. 1070 pages. 9.88x7.24x1.77 inches. In Stock.
Language: English
Published by Cambridge University Press, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
£ 85.57
Quantity: Over 20 available
Add to basketHRD. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Language: English
Published by Cambridge University Press, Cambridge, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: Grand Eagle Retail, Bensenville, IL, 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 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Language: English
Published by Cambridge University Press, Cambridge, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: CitiRetail, Stevenage, United Kingdom
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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Language: English
Published by Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. PRINT ON DEMAND.
Language: English
Published by Cambridge University Press, Cambridge, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: AussieBookSeller, Truganina, VIC, Australia
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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Language: English
Published by Cambridge University Press, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
Seller: preigu, Osnabrück, Germany
Buch. Condition: Neu. Inference and Learning from Data | Ali H. Sayed | Buch | Gebunden | Englisch | 2022 | Cambridge University Press | EAN 9781009218269 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.