Data Modeling: From Physical Processes to Machine Learning - Softcover

Rueckert, Elmar

Softcover

ISBN 10: 9819575915 ISBN 13: 9789819575916

Publisher: Springer, 2026

Synopsis

This book presents the fundamental theories, concepts, and methods of data modeling, bridging physical processes with machine learning predictions. It covers topics such as data collection, storage, analysis, and practical applications of machine learning.

The textbook is designed for first-semester undergraduate students. The material introduces essential concepts in a clear and approachable way, offering a foundation in data-driven decision-making and predictive modeling.

The content is aligned with the lectures of Prof. Dr. Elmar Rueckert and will be expanded further during the lecture series, making it a comprehensive guide to understanding the world of data and its applications.

Structure of the Book: The chapters cover:

• Fundamentals of Data Modeling

• Processes and Data Granularity

• Sensors and Data

• Information Theory

• Data Analysis

• Machine Learning: Data Organization

• Machine Learning: Selected Applications

To support hands-on learning, the book also includes interactive Jupyter Notebooks that illustrate key concepts through practical exercises.

About this title

About the Author

Prof. Dr. Elmar Rueckert is the head of the Cyber-Physical-Systems Institute at the Technical University of Leoben in Austria. He is an experienced researcher and educator in the fields of machine learning, robotics, and intelligent systems. His academic work bridges theory and application, with a focus on data-driven modeling, stochastic deep learning, reinforcement learning, and human motor control.

Prof. Rueckert has extensive experience in university-level teaching. At the Technical University of Leoben, he offers a wide range of courses on topics such as machine learning, deep learning, reinforcement learning, data modeling, and robotics. His teaching is practice-oriented and often supplemented with interactive programming exercises and real-world case studies.

In addition to his teaching activities, Prof. Rueckert leads several national and international research projects. These include collaborations on sustainable recycling technologies, digital twins for industrial systems, intelligent sensor integration, and autonomous systems in robotics. His institute works closely with industry partners, combining fundamental research with applied innovation to address real-world challenges in industrial automation, energy efficiency, and environmental impact. After completing his Ph.D. in computer science at Graz University of Technology with distinction in 2014, he held senior research positions at the Technical University of Darmstadt, where he led a robotics research group. From 2018 to 2021, he served as an assistant professor at the University of Lübeck, where he was responsible for courses in machine learning and reinforcement learning. In recognition of his scientific contributions, he received the prestigious German Young Researcher Award for Artificial Intelligence in 2019.

Since 2021, Prof. Rueckert has been leading the Cyber-Physical-Systems Institute at Montanuniversität Leoben, where he continues to combine teaching excellence with cutting-edge research in artificial intelligence and intelligent systems.

From the Back Cover

This book presents the fundamental theories, concepts, and methods of data modeling, bridging physical processes with machine learning predictions. It covers topics such as data collection, storage, analysis, and practical applications of machine learning.

The textbook is designed for first-semester undergraduate students. The material introduces essential concepts in a clear and approachable way, offering a foundation in data-driven decision-making and predictive modeling.

The content is aligned with the lectures of Prof. Dr. Elmar Rueckert and will be expanded further during the lecture series, making it a comprehensive guide to understanding the world of data and its applications.

Structure of the Book: The chapters cover:

• Fundamentals of Data Modeling

• Processes and Data Granularity

• Sensors and Data

• Information Theory

• Data Analysis

• Machine Learning: Data Organization

• Machine Learning: Selected Applications

To support hands-on learning, the book also includes interactive Jupyter Notebooks that illustrate key concepts through practical exercises.

Edition details

• Publisher: Springer
• Publication date: 2026
• Language: English
• ISBN 10: 9819575915
• ISBN 13: 9789819575916
• Binding: Paperback
• Number of pages: 127