I’d add lecture(s) talking about MAP/MLE, bias-variance trade off, and model interpretability, common pitfalls (Eg. Concept drift), and (maybe) building ml systems.

I’d skip the lectures on reinforcement learning and gans and maybe add a lecture on recommender systems. I’d say you need quite a bit of knowledge on both of these topics before you can actually solve real/practical problems.

Honestly, 16 weeks isn’t a lot of time to learn/digest all of this material in depth. I’d focus a lot more on the practical.