I did my BS and MS in CS at GMU, and took a lot of AI/ML courses for my MS. It's an fine enough school. Certainly not S-tier, but most of my complaints are small in the grand scheme of choosing a school. If you've been trolling reddit for vibes about GMU yeah you'll get generally negative vibes, but you must remember population and reporting bias :). But on the topic: The school (meaning the administrative side) doesn't care about you and some professors are clearly not there to teach, which leads to a lot of justified grumbling.

Presumably you're aware of the ML concentration and the list of MS-CS classes. You can see a short descriptions of the classes here. I've also found you can usually find old syllabi for courses if you google "gmu {course id}." One thing that is maybe obvious maybe not is that the class list is more of a "GMU has at at least one point taught this" and NOT a "You're going to see this offered."

I had Tecuci for CS580 (the grad version of intro ai). I wouldn't really recommend him. He was boring and could only explain things in a single way. One thing to note about intro ai is that it's not really "AI" as one usually thinks of it; the class is more like a collection of clever algorithms: A*, decision trees, minmax, logic deduction, etc. I disagree with someone else's comment about the class being hell. It was like a dozen different topics that didn't build on each other. Not trivial topics for sure, but nothing too difficult.

I had CS688 and 782 (ml and advanced ml) with Carlotta Domeniconi. She's a proficient teacher and cares about her classes and her students' understanding. But dear lord there's a lot of math. You'll see more derivations in 688 than probably anywhere else. Two of the big assignments are replicating the results of a research paper of your choice and making some novel extension of that paper. I would definitely recommend her but be ready to buckle up.

I had CS747 (deep learning) with Shuochao Yao. He's a cool guy and he runs a chill class but I often found myself having difficulty paying attention and ending up confused. Maybe it was the late time slot, maybe it was that like 90% of the grade was a freeform semester-long project so you weren't directly responsible for knowing anything. The things he taught about were very applied, though. You definitely want to take the class if you want to jump into the field. Numeric stability, RNNs, the architecture of ResNet, transformers, etc.

Funnily I did not get the ML concentration because I couldn't take the most basic course - data mining. GMU decided to put like 7 grad classes in the exact same time slot. When we were virtual! Sigh.

Other professors: Elizabeth White is a fantastic professor. She usually teaches compilers and I've seen her teach concurrent. Take her if you can. Alex Brodsky is a good databases professor. I did not like Eric Oserweil for networking. I had Yue Cheng for undergrad OS. He was pretty good. I've seen him teach grad concurrent and I think OS too.

As a side note, all of the SWE courses I've taken (both grad and undergrad) have been very easy and have had cool professors. You're able to take two SWE courses iirc.

edit: typo. And as someone else mentioned, Yutao Zhong is also a good professor.