[D] Open Exposition Problems in Machine Learning
In his paper “A Beginner’s Guide to Forcing,” Timothy Chow introduced the idea of an “open exposition problem,” which is a concept that has not yet been explained in a totally clear way. The online journal Distill is trying to tackle open exposition problems in machine learning, which I feel is really important.
So what do you guys think still isn’t explained well in ML? What topics confuse you or your students?
Timothy Chow’s paper: http://timothychow.net/forcing.pdf