CS148 [1] varies from year to year (most people complained that I made the final assignment on subdivision surfaces too hard, but I had at least a few "Thank you, this was awesome") and is the pre-cursor to 248. I'd recommend it if you're getting started.
If you like ray tracing, Cem Yuksel currently teaches most of the related courses at Utah [2].
There's tons of material online now. If you want a "traditional Utah graphics" curriculum, Cem is teaching most of the stuff (with updates!) from 15 years ago. You can also probably find the old course slides and assignments. I'd suggest cs6620, personally:
CS 140 (hope I remembered the course number right) where you write an operating system mostly from scratch is notoriously time consuming and rewarding, at least back when I was there.
Nowadays, there are a couple of really excellent online lectures to get you started.
The list is too long to include them all. Every one of the major MOOC sites offers not only one but several good Machine Learning classes, so please check
[coursera](https://www.coursera.org/), [edX](https://www.edx.org/),
[Udacity](https://www.udacity.com/) yourself to see which ones are interesting to you.
However, there are a few that stand out, either because they're very popular
or are done by people who are famous for their work in ML. Roughly in order
from easiest to hardest, those are:
* Andrew Ng's [ML-Class at coursera](https://www.coursera.org/course/ml): Focused
on application of techniques. Easy to understand, but mathematically very shallow.
Good for beginners!
* Daphne Koller's [Probabilistic Graphical Models](https://www.coursera.org/course/pgm)
Is a very challenging class, but has a lot of good material that few of the other.
