Model-Based Machine Learning

John Winn

ISBN 10: 1498756816 / ISBN 13: 9781498756815

Published by Chapman and Hall/CRC, 2023

About this Item

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.

Seller Inventory # 127827996

Bibliographic Details

• Title: Model-Based Machine Learning
• Publisher: Chapman and Hall/CRC
• Publication Date: 2023
• Language: English
• ISBN 10: 1498756816
• ISBN 13: 9781498756815
• Binding: Buch
• Condition: Neu
• Dimensions: 240W x 161H x 30D millimeters 240 millimeters width by 161 millimeters height by 30 millimeters depth
• Weight: 866 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.

About the Author

John Winn is a Principal Researcher at Microsoft Research, UK.