I took the EdX/MIT course[1] on optimization methods and constraint solvers using Julia and JuMP and it was really fun. Julia wasn't around, or it was very ear

I took the EdX/MIT course[1] on optimization methods and constraint solvers using Julia and JuMP and it was really fun. Julia wasn't around, or it was very early, when he designed the Machine Learning course or was teaching Stanford students, and he's moved on since then. I wonder if he would likely choose Julia now. I like R, but understand that %*% for matrix multiplication, solve() instead of \, and the relatively cumbersome syntax for defining matrices indicate that it not designed primarily for users to interact with matrices at a lower, mechanical level. The xapply() functions can also be more confusing than picturing how you iterate through loops. Python too can be verbose for doing the simple/toy problems that are helpful when learning. Julia however comes with the easy Matlab/Octave syntax for handling matrices. But then there is a lot of of syntactic sugar too after you get past the early stages. Even things like the built-in support for intermixing Greek letters were surprisingly helpful. I think the advantage of Matlab is the toolboxes in engineering contexts, but that Julia has a similar learning curve for beginners working with matrices. Perhaps the Matlab IDE is an advantage, but that doesn't come with Octave, and Jupyter or Atom+Julia are relatively user-friendly. [1] https://www.edx.org/course/optimization-methods-business-analytics-mitx-15-053x# https://www.edx.org/course/optimization-methods-business-ana...!