Supervised Machine Learning for Science

How to stop worrying and love your black box

Supervised Machine Learning for Science cover image

Supervised Machine Learning for Science is a philosophical and pragmatic justification for applying machine learning models in research.

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Summary

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.

Who This Book Is For

This book is aimed at scientists who use or want to use machine learning. But we believe that anyone who uses machine learning beyond pure prediction will benefit from the book.

Prerequisites:

  • You should know the basics of machine learning
  • Be interested in machine learning beyond prediction

Table of Contents

  • 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