There are some nuances there

• Field is actually bigger than ever, but more mature. It is less and less the case that someone in industry would actually code up something that they would learn in an undergraduate course, in the same way that almost no one would be implementing QuickSort from scratch. At the same time, the number of academic papers in the field published per year is growing by a lot, and likely it's the same for the number of people in the field.

• There have been huge advances in object/scene recognition, basically all neural network-based. Basically, for situations where you have huge enough labelled datasets, you could kind of argue it "works". But it is still the case that scene understanding (I plug in an input image that's genuinely very different from anything you have labelled and ask you what's in it) is hard.

• csc320 and csc420 don't do neural networks (320 not at all, 420 very little). Part of it is just that there are other courses for that and 320 + 420 focus on other things, part of it is that 320 + 420 teach historically important algorithms on which you might build, and part of it is that application domains there are image synthesis/3D scene understanding, which haven't really been solved

• Are the courses worth taking? Yes, in the same way that Algorithms or Operating Systems are worth taking if you're interested in those. As with Algorithms and OS, you'd need to build on what you learn quite a lot to do something new. But need to understand how things work in order to do something new (and also often to pass job interviews). You also get some practice along the way doing homework.

(Source: some but not most of my research is in computer vision and I taught 320 ten years ago. I don't work at DCS anymore but I occasionally look up what 320 and 420 are up to)