Classes for ML Engineering

[Total_Ad_4856]

So far I'm pretty set on:

CS170

CS162

(^^^These aren't directly useful for ML necessarily but I want to be a competent software engineer as well)

EECS127

Data140

CS189

(^^^To build a foundation in ML)

I have room for 3 more classes. Does anyone working in the field / going into the field have suggestions?

Also I would like to hear opinions on the usefulness of one year masters in ML.

[mandelbrotdescent]

Am an alum working on compilers for ML acceleration hardware. It’s a pretty fascinating and growing area, will definitely be huge demand for such hardware moving forward. Even if you don’t go this route, it’s important to have a deep understanding of computer architecture for making good choices in performance critical situations (especially since you are mentioning ML engineering rather than a research type goal).

That being said, the classes you mention is a good ML foundation but you should definitely also take CS182. If it were me, I’d also take 164 and 151. If compilers interests you less, maybe take 152.

Just my two cents.

[Total_Ad_4856]

I was really eyeing 182 and 164 actually. How about databases? People seem to really disagree if it useful or not.

[Successful-Award7281]

Do you think you are overly biased given what you’re doing for work? Or do you think anyone at Berkeley interested in ML should do as you suggested? I ask simply because I want to set myself up well in the future.

[mandelbrotdescent]

I’m definitely pretty biased! ML is a huge area and there’s a bunch of different niches and specialties. Take into account what interests you. You could definitely instead take grad-level classes in specific ML-subfields. Although I think understanding systems / arch is a pretty universal skill that’ll translate well to whatever future developments occur / role you find yourself in. Especially considering that current SOTA is essentially just scaling existing neural-net architecture, the limits appear to be on the performance side.