Model-Based Machine Learning
Autor John Winnen Limba Engleză Hardback – 26 oct 2023
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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Specificații
ISBN-13: 9781498756815
ISBN-10: 1498756816
Pagini: 468
Ilustrații: 512
Dimensiuni: 156 x 234 x 29 mm
Greutate: 1.03 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
ISBN-10: 1498756816
Pagini: 468
Ilustrații: 512
Dimensiuni: 156 x 234 x 29 mm
Greutate: 1.03 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Public țintă
Postgraduate and Professional Practice & DevelopmentCuprins
Introduction. How Can Machine Learning Solve my Problem? 1. A Murder Mystery 2. Assessing People’s Skills Interlude. The Machine Learning Life Cycle 3. Meeting Your Match 4. Uncluttering Your Inbox 5. Making Recommendations 6. Understanding Asthma 7. Harnessing the Crowd 8. How to Read a Model Afterword
Notă biografică
John Winn is a Principal Researcher at Microsoft Research, UK.
Descriere
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.