Prof. Dr. Thomas Bartz-Beielstein is an artificial intelligence expert with 30+ years of experience. He is a professor of applied mathematics at TH Köln in Germany and the director of the Institute for Data Science, Engineering, and Analytics (IDE+A). His research lies in artificial intelligence, machine learning, simulation, and optimization. Hedeveloped the Sequential Parameter Optimization (SPO). SPO integrates approaches from surrogate model-based optimization and evolutionary computing. He has worked on diverse topics from applied mathematics and statistics, design of experiments, simulation-based optimization and applications in domains as water industry, elevator control, or mechanical engineering.

Prof. Dr. Martin Zaefferer is a professor at Duale Hochschule Baden-Württemberg Ravensburg, teaching subjects related to data science in business informatics. Previously, he worked as a consultant at Bartz & Bartz GmbH and as a researcher at TH Köln, where he also studied electrical engineering and automation. He received a PhD from the Department of Computer Science at TU Dortmund University. Subsequently, he developed a keen interest in researching methods from the intersection of optimization and machine learning algorithms. He is passionate about the analysis of complex processes and finding novel solutions to challenging real-world problems.

Prof. Dr. Olaf Mersmann is a professor of data science at TH Köln-University of Applied Sciences in Germany and a member of the Institute for Data Science, Engineering, and Analytics (IDE+A). Having studied physics, statistics and data science, his research interests include landscape analysis for black box optimization problems and industrial machine learning applications. He is one of the developers of the exploratory landscape analysis approach to characterize continuous function landscapes.

Provides hands-on examples that illustrate how hyperparameter tuning can be applied in industry and academia

Gives deep insights into the working mechanisms of machine learning and deep learning

This book is open access, which means that you have free and unlimited access

Includes code that equips readers to achieve better results with less time, costs, and effort