[DaedPsyker]

I mean python has issues (indentation errors isn't one I would list) but that's besides the point isn't it. The Lingua Franca for ML is currently python. Teaching octave, when most things they search for will be python just seems unnecessarily stubborn. Some day it might be Julia but we aren't there yet.

[lern_too_spel]

Indentation errors are a big problem for pedagogy. Imports are a big problem for pedagogy. When you are teaching how algorithms work, you want to implement in a language as close as possible to the language of the domain as possible. Hence, Python is a terrible pedagogical language for linear algebra, and Octave is a reasonable language.

[cypress66]

In a vacuum, maybe

But in the real world

1) Python is the lingua franca for ML. You WILL need to learn python. All other resources are in python. Matlab you'll likely never use again, so it's kind of a waste.

2) Probably more people have existing python knowledge than Matlab knowledge. And if you already know python, and you know python is the lingua franca, it's annoying having to learn Matlab knowing that in the real world you'd be better off with python.

[lern_too_spel]

1. You'll use Matlab in any other linear algebra or numerical methods course as well as in any digital signals course.

2. Optimization algorithms, of which gradient descent is a subset, are deployed in production in many languages, very often not Python.

3. There is almost nothing to learn. For the programming assignments in the course, Octave is used as a succinct DSL for matrix math. The assignments were to simply write the math in a computer and watch what happens when you run the computations.

4. You wouldn't learn Python by completing the programming assignments because you're just calling numerical routines, not dealing with anything else. Writing the code in Python simply adds more opportunity for error with no pedagogical benefit.