Overview
- Lists the tools used in machine learning and their benefits when used in facilities
- Presents a wide range of applications and case studies for different industrial sectors
- Explains the most popular algorithms clearly and succinctly without calculus or matrix/vector algebra
Part of the book series: Management and Industrial Engineering (MINEN)
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Table of contents (9 chapters)
Editors and Affiliations
About the editors
Antonio Sartal is a distinguished researcher in the Department of Business Management and Marketing at the University of Vigo, Spain. He managed the Department of R&D of a food multinational for the past ten years, until he joined REDE, a multidisciplinary research group working on technology management and organizational innovation. His research interests include the intersection of lean thinking, innovation management and Industry 4.0 technologies.
J. Paulo Davim is a Full Professor at the University of Aveiro,Portugal. He is also distinguished as honorary professor in several universities/colleges in China, India and Spain. He has more than 30 years of teaching and research experience in Manufacturing, Materials, Mechanical and Industrial Engineering, with special emphasis in Machining & Tribology. He has also interest in Management, Engineering Education and Higher Education for Sustainability.
Bibliographic Information
Book Title: Machine Learning and Artificial Intelligence with Industrial Applications
Book Subtitle: From Big Data to Small Data
Editors: Diego Carou, Antonio Sartal, J. Paulo Davim
Series Title: Management and Industrial Engineering
DOI: https://doi.org/10.1007/978-3-030-91006-8
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-91005-1Published: 12 March 2022
Softcover ISBN: 978-3-030-91008-2Published: 12 March 2023
eBook ISBN: 978-3-030-91006-8Published: 11 March 2022
Series ISSN: 2365-0532
Series E-ISSN: 2365-0540
Edition Number: 1
Number of Pages: IX, 211
Number of Illustrations: 14 b/w illustrations, 65 illustrations in colour
Topics: Industrial and Production Engineering, Machine Learning, Artificial Intelligence