Informed Machine Learning

ISBN 10: 3031830962 / ISBN 13: 9783031830969

Published by Springer Verlag GmbH, 2025

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Seller Inventory # 2054249751

Bibliographic Details

• Title: Informed Machine Learning
• Publisher: Springer Verlag GmbH
• Publication Date: 2025
• Language: English
• ISBN 10: 3031830962
• ISBN 13: 9783031830969
• Binding: Hardcover
• Condition: New

About this title

Synopsis

This open access book presents the concept of Informed Machine Learning and demonstrates its practical use with a compelling collection of applications of this paradigm in industrial and business use cases. These range from health care over manufacturing and material science to more advanced combinations with deep learning, say, in the form of physical informed neural networks. The book is intended for those interested in modern informed machine learning for a wide range of practical applications where the aspect of small data sets is a challenge.

Machine Learning with small amounts of data? After the recent success of Artificial Intelligence based on training with massive amounts of data, this idea may sound exotic. However, it addresses crucial needs of practitioners in industry. While many industrial applications stand to benefit from the use of AI, the amounts of data needed by current learning paradigms are often hard to come by in industrial settings. As an alternative, learning methods and models are called for which integrate other sources of knowledge in order to compensate for the lack of data. This is where the principle of “Informed Machine Learning” comes into play.

Informed Machine Learning combines purely data driven learning and knowledge-based techniques to learn from both data and knowledge. This has several advantages. It reduces the need for data, it often results in smaller, less complex and more robust models, and even makes machine learning applicable in settings where data is scarce. The kind of knowledge to be incorporated into learning processes can take many different forms, for example, differential equations, analytical models, simulation results, logical rules, knowledge graphs, or human feedback which makes the approach overall very powerful and widely applicable.

About the Author

Daniel Schulz is one of the managing directors of the Fraunhofer Cluster of Excellence Cognitive Internet Technologies CCIT, where he is responsible for the Fraunhofer Technology Hub Machine Learning and works on implementable technology solutions for the edge-cloud continuum. His main research focuses on informed machine learning techniques that not only learn from data but can also utilize existing knowledge and models. In addition, Daniel Schulz represents the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS) at the Scientific and Technical Council of the Fraunhofer Society. He studied Geosciences at the Universities of Cologne, Bonn and Gothenburg, and has today 15+ years of experience as a senior data scientist in industry and public funded projects in various industries and research fields.

Christian Bauckhage is a professor of computer science (intelligent learning systems) at the University of Bonn, lead scientist for machine learning at Fraunhofer IAIS, and one of the directors of the Lamarr Institute for Machine Learning and Artificial Intelligence. He has 20+ years of experience as a data scientist in industry and academia and (co)authored numerous publications on pattern recognition, data mining, and machine learning. His current research focuses on informed machine learning techniques that integrate knowledge- and data-driven methods. Practical applications of his work can be found in fields as diverse as physics, agriculture, or business analytics. As an expert on applied AI, he frequently consults private and public institutions regarding the design and deployment of intelligent systems.