Some of my colleagues are unfamiliar with the ML paradigm, so I wrote a short summary that introduces the basics and explains how I plan to apply ML in our domain. It draws in large part on what I learned in Vijay’s courses, which did a lot to crystalize my ideas.
I’ll pass along an article I came across that frames the distinction between traditional scientific models and ML in a compelling way:
“New machine learning theory raises questions about [the] nature of science”
(I would have removed the word “New” from the title.)
The ‘money’ quote – and likely a controversial assertion – that jumped out at me:
“I would argue that the ultimate goal of any scientist is prediction,” [Princeton physicist Dr. Hong] Qin said. "You might not necessarily need a law. For example, if I can perfectly predict a planetary orbit, [then] I don’t need to know Newton’s laws of gravitation and motion.
Within the context of sustainable aquaculture, my interpretation is here. (NB: It’s on Heroku’s free tier, so it doesn’t open immediately.)