Not OP but I deal with this a lot. In my experience a lot of folks working in mainstream ML haven't been exposed to it unless they specifically focused on it. It might just be a course load thing... getting the most out of these probabilistic PLs requires fairly deep expertise in both probability theory/Bayesian stats as well as in CS and you have a finite amount of courses you can take in school. Plus, a lot of the work in this area pre-dates the modern focus on deep learning or machine learning in general, so a lot of the knowledge tends to be held by professors/researchers that may not be as involved with the "new" ML courses. And of course, Math/Stats/CS departments don't always play nicely with each other and like to fight turf wars, though I've noticed cross-disciplinary research among the three becoming more accepted at the universities/institutes I work with.

As a case study, I did most of my grad work on solving Bayesian inverse problems using probabilistic programming for applications in engineering, which is pretty cross-disciplinary. I now work mostly in ML, but I didn't really even touch anything in the ML domain until after I finished school. I could have, the courses were available, but they just weren't relevant to me at the time.

One of the best ML researcher I know has a background in signal processing (and degrees in EE to go with). Not probability per se, but heavily uses probability and statistics.

> has a background in signal processing

Which largely counts as strong Linear Algebra and Probability Theory background.

While there are some exception, majority of published deep learning research barely mentions statistics at all, it's optimization all the way down

ML is fundamentally not a CS specialty. It is a statistics/optimization (thus applied math) specialty.

CS only comes into the picture at runtime. ML theory is divorced from computability until then.

That's a weird game to play with those words.

Can you elaborate? The unreasonable effectiveness of approximate methods on discretized spaces doesn't change the fact that the theory underlying it is exact and continuous.

ML is a sub field of CS, in practice.

You don’t need a professional license to do math. Lots of computer scientists to harder and more interesting mathematics than their peers in the math dept. In that respect at least, the main substantive difference between the fields is about $40k/yr.

> ML is a sub field of CS, in practice.

You're just stating things without justifying them.

Again, this is such a weird game to play with words. I'm not sure what else to say.

ML people nowadays barely know basic stats.

Disappointed black guy meme upon realizing a lot of “data science” is just calling some scikit learn module lol

lmao please link disappointed black guy meme appropriate for the times haha

My bad: https://knowyourmeme.com/memes/disappointed-black-guy

ML is going mainstream. Most programmers don't know algorithms and data structures, I bet, if you consider all programmers around the world.

Normalcy is not a substitute for correctness.

> ML people nowadays barely know basic stats.

The same can unfortunately be said of many "statisticians", who use statistics as a big recipe book without understanding the first thing about the mathematical underpinnings of the topic.

Don't believe me?

Go ask the first statistician you run into to give you a half decent of how the Chi-squared distribution and the Chi-squared test works, see what happens.