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.

Looking at an ML Engineer role with the following qualifications:

"Strong experience in the area of developing machine learning training framework, or hardware acceleration of machine learning tasks"

"Familiar with hardware architecture, cache utilization, data streaming model"

Any recommendations for books/resources/courses in this area? How does one begin to develop these skills?

As someone who’s also 27 and been debating whether/not to start a PhD, this is reassuring to hear!

Most of the people I know started their PhD right after undergrad. The grad student I worked with during my undergrad was actually 26 when he graduated with his PhD.

That being said, CS and (& specifically ML) seems to have younger PhD students than other fields.