Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications - Softcover
Synopsis
Machine learning and artificial intelligence are ubiquitous terms for improving technical processes. However, practical implementation in real-world problems is often difficult and complex.
This textbook explains learning methods based on analytical concepts in conjunction with complete programming examples in Python, always referring to real technical application scenarios. It demonstrates the use of physics-informed learning strategies, the incorporation of uncertainty into modeling, and the development of explainable, trustworthy artificial intelligence with the help of specialized databases.
Therefore, this textbook is aimed at students of engineering, natural science, medicine, and business administration as well as practitioners from industry (especially data scientists), developers of expert databases, and software developers.
"synopsis" may belong to another edition of this title.
About the Author
Dr. Marcus J. Neuer has developed machine learning and explainable artificial intelligence for usable, profitable applications in various research and industry projects. He leads the research and development department at innoRIID GmbH and teaches at RWTH Aachen as well as the University of Applied Sciences for Business, FHDW. His algorithms are successfully used today in various products, including in the fields of nuclear safety and the process industry.
From the Back Cover
Machine learning and artificial intelligence are ubiquitous technologies for improving technical processes. However, practical implementation in real-world problems is often difficult and complex.
This textbook explains learning methods based on analytical concepts in conjunction with complete programming examples in Python, always referring to real technical application scenarios. It demonstrates the use of physics-informed learning strategies, the incorporation of uncertainty into modeling, and the development of explainable, trustworthy artificial intelligence with the help of specialized databases.
Therefore, this textbook is aimed at students of engineering, natural sciences, medicine, and business administration as well as practitioners from industry (especially data scientists), developers of expert databases, and software developers.
Excerpts from the Content
- Introduction to Working with Data
- Mathematical Fundamentals and Data Preprocessing
- Supervised and Unsupervised Learning Methods
- Physics-Informed and Stochastic Learning Methods
- Semantic Technologies and Explainable, Trustworthy Artificial Intelligence
The Author
Dr. Marcus J. Neuer has developed machine learning and explainable artificial intelligence for usable, profitable applications in various research and industry projects. He leads the research and development department at innoRIID GmbH and teaches at RWTH Aachen as well as the University of Applied Sciences for Business, FHDW. His algorithms are successfully used today in various products, including in the fields of nuclear safety and the process industry.
The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.
"About this title" may belong to another edition of this title.
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Taschenbuch. Condition: Neu. Machine Learning for Engineers | Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications | Marcus J. Neuer | Taschenbuch | xvii | Englisch | 2024 | Springer | EAN 9783662699942 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Seller Inventory # 129793060