9781498756815 - Model-Based Machine Learning di Winn, John (26 risultati)

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Hardback. Condizione: 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.

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Condizione: New. 2023. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

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Condizione: 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.

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

Buch. Condizione: 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.

Hardback. Condizione: 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.

Hardcover. Condizione: new. Hardcover. 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. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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

Hardcover. Condizione: new. Hardcover. 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. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Buch. Condizione: 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.