Optimization and Machine Learning: Optimization for Machine Learning and Machine Learning for Optimization
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Author Description
Machine learning and optimization techniques are revolutionizing our world. Other types of information technology have not progressed as rapidly in recent years, in terms of real impact. The aim of this book is to present some of the innovative techniques in the field of optimization and machine learning, and to demonstrate how to apply them in the fields of engineering. Optimization and Machine Learning presents modern advances in the selection, configuration and engineering of algorithms that rely on machine learning and optimization. The first part of the book is dedicated to applications where optimization plays a major role, and the second part describes and implements several applications that are mainly based on machine learning techniques. The methods addressed in these chapters are compared against their competitors, and their effectiveness in their chosen field of application is illustrated.
Menrva's Summary
THE THESIS
This book explores the synergistic relationship between optimization and machine learning, demonstrating their transformative impact on engineering applications. The authors present innovative techniques in both fields, dedicating the first part to optimization's role and the second to machine learning applications. Methods are compared against competitors, and their effectiveness is illustrated through practical examples, showcasing modern advances in algorithm selection and configuration.
THE SCOPE MAP
- Optimization techniques applied to engineering problems.
- Machine learning techniques and their implementation in engineering.
- Comparison of different algorithms and their effectiveness.
- Modern advances in algorithm selection and configuration.
THE AUTHORITY PERSPECTIVE
The book presents modern advances in optimization and machine learning, focusing on their application in engineering. It is structured into two parts, with the first detailing optimization's role and the second focusing on machine learning techniques. The authors compare various methods against their competitors, illustrating their effectiveness in chosen fields. The material suggests a technical and practical approach to these rapidly evolving fields.
Page Count:
256
Publication Date:
2022-04-19
Publisher:
John Wiley & Sons
ISBN-10:
1789450713
ISBN-13:
9781789450712
Science
Non-Fiction
Engineering
Machine Learning
Algorithms
Optimization
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