Preface

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

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 proper 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 a journey through the philosophy of science, machine learning theory, pragmatic modeling advice, and short stories of our raven scientists.