College of Statistics and Nonlinear Function Approximation
Unfortunately, I think statistics will continue to be under-appreciated. All of my mathematics professors in undergraduate said that statistics and linear algebra are the two most useful fields of math to know and that dedicating time to studying them will pay dividends. It still surprises me when I apply for jobs today how few places, even financial or scientific firms, distinguish between statistics and mathematics…
I don't know. I studied a lot of Machine Learning in UC Berkeley (even though my specialization was on systems since I like it more) and it was all very rigorous linear algebra, probability theory, optimization, signal processing, information theory, statistics, algorithm analysis etc... Sure we also took classes about designing heuristics, data visualization etc but they were no where near as serious/hard as other classes, so students focused on other classes. Pretty much all students who were serious about ML took upperdivision Linear Algebra, Abstract Algebra and/or Analysis classes. We all took EE, Stats, CS, Data Science etc... and saw ML from bunch of different aspects (e.g. EE perspective being more signal/information -esque, or CS perspective more computational (kernels!) etc...). I have no reason to believe MIT will be any less rigorous. I think most (almost all?) random ML intros online are filler courses without much relation "Actual" (?) ML, but I have no reason believe something from MIT, Berkeley, Stanford, CMU etc will be like that.