He didn't say he found it personally challenging. He said this part of optimization finds itself at the intersection of three deep fields of human knowledge. That is undeniably correct.
Right, but in this particular case the "stochastic" and "algorithm" parts are trivial. For example, you don't really need to draw on CS/algorithm knowledge to implement or understand gradient descent.
I don't think it was pretentious, and your analogy is quite off. You don't need to understand any of those fields to grok bubblesort (algorithmics is enough). You do need to know some statistics, calculus and algorithmics to understand stochastic gradient descent.
you don't need to understand statitstics. calculus (calculus is a high school subject and is hardly deep), and what is "algorithmics"? If you mean the study of any and all algorithms, then sure,
