Supervised Machine Learning for Science
Supervised Machine Learning for Science
How to stop worrying and love your black box
About the Book
Machine learning has revolutionized science, from folding proteins and predicting tornadoes to studying human nature. While science has always had an intimate relationship with prediction, machine learning amplified this focus. But can this hyper-focus on prediction be justified? Can a machine learning model be part of a scientific model? Or are we on the wrong track?
In this book, we explore and justify supervised machine learning in science. However, a naive application of supervised learning won’t get you far because machine learning in raw form is unsuitable for science. After all, it lacks interpretability, causality, uncertainty quantification, and many more desirable attributes. Yet, we already have all the puzzle pieces needed to improve machine learning, from incorporating domain knowledge to creating robust, interpretable, and causal models. The problem is that the solutions are scattered everywhere.
In this book, we bring together the philosophical justification and the solutions that make supervised machine learning a powerful tool for science.
The book consists of two parts:
- Part 1 justifies the use of machine learning in science.
- Part 2 discusses how to integrate machine learning into science.
About the Authors
Reader Testimonials
Bemah, Ibrahim
Mining, Geology and Civil Engineering Practitioner and Researcher University of South Australia
Supervised Machine Learning for Science by Christoph and Timo is essential for professionals in high-stakes fields like mining and civil engineering. The book addresses the need for responsible, interpretable machine learning, focusing on domain knowledge, causality, and uncertainty—bridging advanced analytics with the accountability our work demands.
Carolina Natel de Moura
Postdoctoral Researcher at Karlsruhe Institute of Technology (KIT) and trainer at the Digital Research Academy
This book was invaluable as I prepared ML training materials for PhD students. It covers both the basics and real-world challenges, presenting complex topics in an accessible way. Ideal for beginners and experienced ML practitioners alike, it's the best guide I’ve found for researchers aiming to achieve their scientific goals with ML.
Table of Contents
- Summary
- Preface
- 1 Introduction
- 2 Bare-Bones Supervised Machine Learning
- Justifying Machine Learning For Science
- 3 The Role of Prediction in Science
- 4 Justification to Use Machine Learning
- 5 Machine Learning and Other Scientific Goals: A Clash
- 6 Bare-Bones Machine Learning is Insufficient
- Integrating Machine Learning Into Science
- 7 Generalization
- 8 Domain Knowledge
- 9 Interpretability
- 10 Causality
- 11 Robustness
- 12 Uncertainty
- 13 Reproducibility
- 14 Reporting
- 15 The Future of Science in the Age of Machine Learning
- Acknowledgments
- Citing this Book
- About the Authors
- References
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