Supervised machine learning for science with Christoph Molnar and Timo Freiesleben, Part 2

The AI Fundamentalists

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.

What did you think? Let us know.

Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics:

• LinkedIn - Episode summaries, shares of cited articles, and more.
• YouTube - Was it something that we said? Good. Share your favorite quotes.
• Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

Chapters

1. Introduction to supervised ML for science (00:00:00)

2. The model as the expert? (00:01:00)

3. Measuring causality: Metrics and blind spots (00:10:10)

4. Connecting models to scientific understanding (00:18:00)

5. Robustness across distribution shifts (00:26:40)

6. Reproducibility challenges in ML and science (00:35:00)