Model-Based Machine Learning (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. Key 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.

General

Imprint:	Productivity Press
Country of origin:	United States
Release date:	October 2023
First published:	2018
Authors:	John Winn
Dimensions:	234 x 156mm (L x W)
Format:	Hardcover
Pages:	410
ISBN-13:	978-1-4987-5681-5
Categories:	Books > Computing & IT > General Books > Science & Mathematics > Mathematics > Probability & statistics Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General Promotions
LSN:	1-4987-5681-6
Barcode:	9781498756815

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