1  Preface

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We wrote this book because we are passionate about science and machine learning, particularly their interaction.

Timo’s mathematical background, deepened by his study of the philosophy of science, complements Christoph’s journey from statistics to machine learning. Although our paths are similar, they differ in complementary ways: Timo takes a theoretical and philosophical approach, while Christoph focuses on the applied and methodological.

Our collaboration began during our Ph.D. studies, where we co-authored papers on interpretable machine learning. However, we soon realized that interpretability was but one piece of a puzzle. Exploring causality, uncertainty quantification, robustness, and other tools proved essential to convert ‘raw’ machine learning into a scientific tool. The deeper we dug, the clearer it became that an even bigger piece was missing: A strong justification for using machine learning in scientific modeling was needed. All our insights led us to this book.

Prepare for an unprecedented journey through the philosophy of science, machine learning theory, pragmatic modeling advice, and short stories of our raven scientists.