Model Based Machine Learning by Winn John (20 results)

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  • Language: English

    Published by Chapman and Hall/CRC, 2023

    1498756816 / 9781498756815

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  • Language: English

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  • Language: English

    Published by CRC Press, 2023

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  • Language: English

    Published by Chapman and Hall/CRC, 2023

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  • Language: English

    Published by Chapman and Hall/CRC, 2023

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  • Language: English

    Published by Taylor & Francis Group, 2023

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  • Language: English

    Published by Chapman and Hall/CRC 2023-10-26, 2023

    1498756816 / 9781498756815

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  • Language: English

    Published by Chapman and Hall/CRC, 2023

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  • Language: English

    Published by Taylor & Francis Inc, 2023

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  • Language: English

    Published by Chapman and Hall/CRC, 2023

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  • Language: English

    Published by Chapman and Hall/CRC, 2023

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  • Language: English

    Published by Taylor & Francis Group, 2023

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  • Language: English

    Published by Taylor and Francis Inc, US, 2023

    1498756816 / 9781498756815

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    Hardback. Condition: New. Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system.The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem.Features:Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems.Explains machine learning concepts as they arise in real-world case studies.Shows how to diagnose, understand and address problems with machine learning systems.Full source code available, allowing models and results to be reproduced and explored.Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.

  • Language: English

    Published by Taylor & Francis Group, 2023

    1498756816 / 9781498756815

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  • Language: English

    Published by CRC Press, 2023

    1498756816 / 9781498756815

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    Condition: New. John Winn is a Principal Researcher at Microsoft Research, UK.Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is conn.

  • Language: English

    Published by Chapman & Hall, 2021

    1498756816 / 9781498756815

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    Seller: Revaluation Books, Exeter, United KingdomRevaluation Books

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    Hardcover. Condition: Brand New. 400 pages. 10.00x7.00x1.00 inches. In Stock.

  • Language: English

    Published by Taylor and Francis Inc, US, 2023

    1498756816 / 9781498756815

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    Hardback. Condition: New. Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system.The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem.Features:Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems.Explains machine learning concepts as they arise in real-world case studies.Shows how to diagnose, understand and address problems with machine learning systems.Full source code available, allowing models and results to be reproduced and explored.Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.

  • Language: English

    Published by Chapman & Hall, 2021

    1498756816 / 9781498756815

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    Seller: Revaluation Books, Exeter, United KingdomRevaluation Books

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    Hardcover. Condition: Brand New. 400 pages. 10.00x7.00x1.00 inches. In Stock. This item is printed on demand.

  • Language: English

    Published by Chapman And Hall/CRC, 2023

    1498756816 / 9781498756815

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    Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to real world problems. This book tackles this challenge through model-based machine learning, focusing on understanding the assumptions encoded in a machine learning system.

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    Language: English

    Published by Chapman and Hall/CRC, 2023

    1498756816 / 9781498756815

    • Hardcover
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    Buch. Condition: Neu. Model-Based Machine Learning | John Winn | Buch | Einband - fest (Hardcover) | Englisch | 2023 | Chapman and Hall/CRC | EAN 9781498756815 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.