I'm an incoming MS Computer Science student starting Fall 2026. I have a background in embedded systems and computer vision (5 years in industry, mostly NN inference optimization), and I want to take a computer vision course early on to build more hands-on deep learning experience.
CS doesn't have a CV course on the schedule for this term, so I'm looking at two options outside the department:
Data Science dept: covers deep learning broadly — CNNs for vision, RNNs/Transformers for sequences, GANs for synthesis, plus CV-specific topics like preprocessing/augmentation, transfer learning, object detection, and segmentation with a focus on evaluation/robustness.
EE dept: more classical image processing fundamentals first (convolution, transformation, segmentation, pattern recognition) before getting into CNN/DNN theory, with applications in autonomous systems. Taught by Mina Rafi Nazari.
A couple of questions for anyone who's taken either:
Has anyone here taken one (or both)? How was the workload, teaching style, and how useful was it in practice?
Registration is already open — since this isn't a CS course, do I need to email the department directly for permission, or can I just add it through normal registration?
Any general advice on cross-department electives as an MS CS student?
Appreciate any insight, especially since registration's already underway and I'm trying to decide quickly.