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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.
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