Building out an ML syllabus
Deciding what to study, and what order to study things in is hard: a concrete project focuses the mind wonderfully, but it doesn’t help when I want to be prepared to work in an area and not have to constantly build expertise along the way.
I could theoretically do this the hard way, with a significant amount of hit and trial, semi-arbitrary experiments and fumbling my way through multiple textbooks as I skim them. Instead, I’m going to take a small shortcut and look through the syllabus from different ML courses and degrees around the world.
Good articles collecting multiple resources:
https://medium.com/technomancy/the-blunt-guide-to-mathematically-rigorous-machine-learning-c53263d45c7b — I like this one a lot.
Degrees/courses I looked at: