Machine learning is a great tool to solve problems, but how does it compare to traditional mathematical modelling? Does one win out or is there a way to combine the two? I’ve written a short (interactive) post which explores these ideas, using a very simple example (far, far simpler than some of the exciting projects I see here).

Available here: Machine Learning and Mathematics: Collaborators or Competitors?

https://i.redd.it/k4vanh2jnky21.png

I’d be very interested to hear what this community thinks about the issue, in particular efforts to try to explain machine learning models rather than creating interpretable models based on underlying scientific background knowledge. From my perspective as an applied mathematician we should build mathematical models as far as we can, and use machine learning to fill in our gaps of knowledge. This allows us to follow the scientific method of hypothesis, testing, and modification, as well as giving perspective when our predictions fail.

Soon after I posted this, the article below was published in Nature Machine Intelligence. The arguments are similar, although the examples are much closer to traditional applications of ML (e.g. image recognition).

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead (Arxiv version)