Part 2 of this series could have easily been renamed "AI for science: The expert’s guide to practical machine learning.” We continue our discussion with Christoph Molnar and Timo Freiesleben to look at how scientists can apply supervised machine learning techniques from the previous episode into their research.

Introduction to supervised ML for science (0:00)

Welcome back to Christoph Molnar and Timo Freiesleben, co-authors of “Supervised Machine Learning for Science: How to Stop Worrying and Love Your Black Box”

The model as the expert? (1:00)

Evaluation metrics have profound downstream effects on all modeling decisions
Data augmentation offers a simple yet powerful way to incorporate domain knowledge
Domain expertise is often undervalued in data science despite being crucial

Measuring causality: Metrics and blind spots (10:10)

Causality approaches in ML range from exploring associations to inferring treatment effects

Connecting models to scientific understanding (18:00)

Interpretation methods must stay within realistic data distributions to yield meaningful insights

Robustness across distribution shifts (26:40)

Robustness requires understanding what distribution shifts affect your model
Pre-trained models and transfer learning provide promising paths to more robust scientific ML

Reproducibility challenges in ML and science (35:00)

Reproducibility challenges differ between traditional science and machine learning

Go back to listen to part one of this series for the conceptual foundations that support these practical applications.

Check out Christoph and Timo's book “Supervised Machine Learning for Science: How to Stop Worrying and Love Your Black Box” available online now.

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