The most common issues applying ML - Mike Walmsley

Building ML models is easy; answering science questions with them is hard. This short talk will introduce common issues in applying ML, illustrated with real failures from astronomy and healthcare - including some by the speaker. We hope sharing the lessons learned from these failures will help participants build useful models in their own field.

Invited Talk by Mike Walmsley

This talk was part of the workshop "Real-world Perspectives to Avoid the Worst Mistakes using Machine Learning in Science" at Pydata Global 2022, organised by Jesper Dramsch, Gemma Turon, and Valerio Maggio.

The workshop has received funding from the Software Sustainability Institute through the 2022 fellowship programme received by Jesper Dramsch.

Find the programme of the workshop, transcripts and more resources here:

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