To be clear, I don't care about the particular courses, I care about the skills. 

This has been fixed, thanks.

I'd like to point out that for neural networks, isolated critical points (whether minima, maxima, or saddle points) basically do not exist. Instead, it's valleys and ridges all the way down. So the word "basin" (which suggests the geometry is parabolic) is misleading. 

Because critical points are non-isolated, there are more important kinds of "flatness" than having small second derivatives. Neural networks have degenerate loss landscapes: their Hessians have zero-valued eigenvalues, which means there are directions you can walk along that don't change the loss (or that change the loss by a cubic or higher power rather than a quadratic power). The dominant contribution to how volume scales in the loss landscape comes from the behavior of the loss in those degenerate directions. This is much more significant than the behavior of the quadratic directions. The amount of degeneracy is quantified by singular learning theory's local learning coefficient (LLC). 

In the Bayesian setting, the relationship between geometric degeneracy and inductive biases is well understood through Watanabe's free energy formula. There's an inductive bias towards more degenerate parts of parameter space that's especially strong earlier in the learning process.

Anecdotally (I couldn't find confirmation after a few minutes of searching), I remember hearing a claim about Darwin being particularly ahead of the curve with sexual selection & mate choice. That without Darwin it might have taken decades for biologists to come to the same realizations. 

You can find a v0 of an SLT/devinterp reading list here. Expect an updated reading list soon (which we will cross-post to LW).