Modeling with Christoph Molnar

Episode 4. The AI Fundamentalists welcome Christoph Molnar to discuss the characteristics of a modeling mindset in a rapidly innovating world. We hope you enjoy this enlightening discussion from a model builder's point of view.

Christoph is the author of multiple data science books including Modeling Mindsets, Interpretable Machine Learning, and his latest book Introduction to Conformal Prediction with Python. To keep in touch with Christoph's work, subscribe to his newsletter The Mindful Modeler

Show notes

Introduction. 0:03

  • Introduction to the AI fundamentalists podcast.
  • Welcome, Christopher Molnar

What is machine learning? How do you look at it? 1:03

  • AI systems and machine learning systems.
  • Separating machine learning from classical statistical modeling.

What’s the best machine learning approach? 3:41

  • Confusion in the space between statistical learning and machine learning.
  • The importance of modeling mindsets.
  • Different approaches to using interpretability in machine learning.
  • Holistic AI in systems engineering.

Modeling is the most fun part but also the beginning. 8:19

  • Modeling is the most fun part of machine learning.
  • How to get lost in modeling.

How can we use the techniques in interpretable ML to create a system that we can explain to stakeholders that are non-technical? 10:36

  • How to interpret at the non-technical level.
  • Reproducibility is a big part of explainability.

Conformal prediction vs. interpretability tools. 12:51

  • Explanability to a data scientist vs. a regulator.
  • Interoperability is not a panacea.
  • Conformal prediction with Python.
  • Roadblocks to conformal prediction being used in the industry.

What’s the best technique for a job in data science? 17:20

  • The bandwagon effect of Netflix and machine learning.
  • The mindset difference between data science and other professions.

Machine learning is always catching up with the best practices in the industry. 19:21

  • The machine learning industry is catching up with best practices.
  • Synthetic data to fill in gaps.
  • The barrier to entry in machine learning.
  • How to learn from new models.

How to train your mindset before you start modeling. 23:52

  • The importance of simplifying two different mindsets.
  • Introduction to conformal prediction with Python.