With Matlab, you can immediately create a Matrix and transpose it, find the inverse...etc. I think it is optimized really well for undergraduate excercises that are really simple (I have two numerical methods textbooks which use Matlab fine). I agree that anything more complex than a couple of functions and a loop is probably better off somewhere else in a lot of cases. With that being said, it has built-in support for GUIs, sparse matrices and other stuff which is a lot harder in C++, so easy to see why it is popular for academic and R&D type work. For a student, the commercial cost is only ~$30 or free at a lot of schools, so usually not a barrier.

You seem to not consider a major player in the field: Python. There's probably a reason Andrew Ng switched from Matlab to Python for teaching.

I literally mentioned Python in one of my above comments lol and it is my daily driver. Still, as an undergrad with zero programming experience, Matlab was very easy to use for the kind of things that undergrads do in engineering and physics (play with matrices, differential equations, make charts...etc). I would agree that machine learning is likely to be easier in Python, but then again, ML students already know how to code, understand OO, and other concepts. Matlab is popular where people are good at math, but have no clue what objects are.