That happens in statistics programs. However, I have a ML-heavy minor in CS, and based on the ML course contents at our CS dept I've seen, I'm not sure if the all their CS majors go through the the full canonical statistics curriculum, nor that they were intended to. At least the ML courses had quite much introductory probability and statistics as far as ML applications were concerned, so I understood the implication was they didn't assume that the students would have already done the similar stuff in statistics (though it certainly helped), and I can't remember a single mention of p-value there.

And then there's this, that even if your intro to probability course everywhere covers the classic statistics with p-values and hypothesis testing and frequentist confidence intervals and so on, you are not necessarily going to use them that much. I calculated some p-values and other tests with R for some example datasets a couple of years ago and never seen them since in coursework, everything we've done after that has been more or less fully Bayesian. The concepts are still fresh[1] in my mind mostly because I read some statistics blogs, such as Andrew Gelman's [2]. The irony is that Gelman does not exactly love frequentist framework, he just mentions its concepts often enough.

[1] or not totally forgotten

[2] http://andrewgelman.com/