[mystique]

A decent CS undergrad degree decade ago included abstract math concepts. I took Engineering math, Information Theory, Numerical analysis, Probability, Simulation in my sophomore and Junior years. NLP and AI were electives in Senior year.

As a Junior, we were building toy programs that do Operations research type of work - solving linear equations via various matrix operations, design optimal queue processes based on poisson process.

Assuming a software engineer is a CS undergrad, he/she most likely has good footing to learn more by themselves.

[argonaut]

In all fairness, that's pretty atypical of a standard CS degree. In my anecdotal experience (knowing people that went to Stanford/Berkeley/MIT/CMU), most people take at most 1 probability class, 1 linear algebra class, and maybe 1 AI/ML class. Info theory, NLP, numerical analysis, optimization, etc. are not at all common.

[Animats]

Or just got a CS degree too long ago. I got lots of discrete math - formal methods, automata theory, and number theory. All that stuff that's in Knuth. But no number-crunching beyond matrix inversion and Fourier transforms.

[sotojuan]

Bad CS school student here, we don't take any Math aside from a very basic "discrete structures" class, which is simplified discrete structures :/ Wish we did more math...

[MathsOX]

That all seems like quasi-maths, however. What hopefully was being referred to earlier is algebra and analysis, at least up to the 2nd iteration, so one has real understanding of methods of proof.

E.g - no probability class that doesn't require analysis 1 and 2 is truly a probability class.