Machine Learning for Engineers
Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications
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Beschreibung
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.
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.
Algorithms Illustrated with Fully Executed Program Examples in Python Explainable and Trustworthy AI for Technical Processes With Programming Exercises and Solutions
Autor*in
Marcus J. Neuer
Themen in »Machine Learning for Engineers«
Data Science Python Machine Learning Reinforced Learning Unsupervised Learning Explainable AI Supervised Learning Stochastics Learning Methods Artificial Intelligence