HACKER Q&A
📣 riverfish

Which course should I take for ML engineering?


Next semester will be my last and my goal is to work as an ML engineer after graduation. I'm currently torn between three courses to choose for my last semester: graduate probability theory, distributed systems or software foundations (covers programming language foundations and logic).

My initial thought was to take probability theory since math is usually harder to learn on your own. On the other hand, I've heard distributed systems is such a key part of ML engineering and the software foundations course is taught by the man who essentially wrote the bible on the topic, Benjamin Pierce.

These are the related courses I've already taken:

ML-related - ML, NLP, intro probability, linear algebra, statistical inference, independent research in few shot learning

Systems-related - OS, networks

PL-related - functional programming in Haskell

Ideally I would pick all three, but alas, I can only pick one. Which one should it be?

  👤 m23khan Accepted Answer ✓
I would go with probability theory but try to probe your Instructor through the course to see if they can give you more insight/advice on how these concepts are translated into ML.

You are right - outside of classroom setting, it is quiet difficult to learn maths on your own and yes, maths (especially probability and stats) are precursor to become good at ML and Data Science.