The argument that this is 90% of what matters in ML seems a bit bold. AFAICT it is completely missing reinforcement learning, which has been source of some mindblowing results (from Deepmind) in the past decade. It is also missing other stuff that I as a layman found fascinating (graph neural networks, 1.5 bit networks, ..). Also, I am not sure how much the Kolmogorov complexity actually matters, it's more philosophical than practical. So beware, it is a very opinionated list.