Machine Learning Algorithms and Applications in Engineering

Machine Learning (ML) is a branch of artificial intelligence that uses soft computing and algorithms to enable computers to learn on their own, identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models. This book discusses various applications of ML in engineering fields and the use of ML algorithms in solving challenging engineering problems.

Format: Hardback
Length: 314 pages
Publication date: 28 February 2023
Publisher: Taylor & Francis Ltd

Machine Learning (ML) is a dynamic and rapidly evolving subfield of artificial intelligence that harnesses the power of soft computing and algorithms to empower computers to acquire knowledge and insights independently. By leveraging observed data, ML enables machines to identify patterns, construct models that elucidate the underlying mechanisms of the world, and make predictions without relying on predefined rules and models. This comprehensive book delves into diverse applications of ML across engineering domains, showcasing the utilization of ML algorithms in addressing complex engineering challenges spanning biomedical, transportation, supply chain, logistics, manufacturing, and industrial sectors. Through a series of captivating case studies, it serves as a valuable resource for researchers and practitioners, equipping them with the necessary tools and strategies to select the most suitable options and approaches for effectively managing organizational tasks.

The field of machine learning (ML) is a rapidly advancing branch of artificial intelligence that leverages the power of soft computing and algorithms to enable computers to learn and adapt on their own. By analyzing vast amounts of data, ML algorithms can identify patterns, make predictions, and construct models that explain the world around us. This book explores the diverse applications of ML in engineering fields, including biomedical engineering, transportation engineering, supply chain engineering, logistics engineering, manufacturing engineering, and industrial engineering. Through detailed case studies and practical examples, it provides researchers and practitioners with the knowledge and skills necessary to select the most appropriate ML algorithms and strategies for solving complex engineering problems.

Machine learning (ML) is a rapidly evolving subfield of artificial intelligence that empowers computers to learn and adapt on their own through the use of soft computing and algorithms. By analyzing vast amounts of data, ML algorithms can identify patterns, make predictions, and construct models that explain the world around us. This comprehensive book explores the diverse applications of ML in engineering fields, including biomedical engineering, transportation engineering, supply chain engineering, logistics engineering, manufacturing engineering, and industrial engineering. Through detailed case studies and practical examples, it provides researchers and practitioners with the knowledge and skills necessary to select the most appropriate ML algorithms and strategies for solving complex engineering problems.

Machine learning (ML) is a rapidly advancing subfield of artificial intelligence that leverages the power of soft computing and algorithms to enable computers to learn and adapt on their own. By analyzing vast amounts of data, ML algorithms can identify patterns, make predictions, and construct models that explain the world around us. This comprehensive book explores the diverse applications of ML in engineering fields, including biomedical engineering, transportation engineering, supply chain engineering, logistics engineering, manufacturing engineering, and industrial engineering. Through detailed case studies and practical examples, it provides researchers and practitioners with the knowledge and skills necessary to select the most appropriate ML algorithms and strategies for solving complex engineering problems.

Weight: 770g
Dimension: 234 x 156 (mm)
ISBN-13: 9780367569129