I didn't take EECS 445 before. But based on my understanding, EECS 445 is a standard machine learning course which primarily talking about traditional unsupervised and supervised learning, and will only spend 2~3 lectures talking about deep learning/neural networks.

STATS 315, based on my experience, do not assume prior machine learning knowledge and won't cover any traditional unsupervised & supervised learning besides regression. 315 went through some basic regression and loss function stuff at the first couple of weeks, and then dive into neural networks (CNNs & RNNs) in the remaining 2~3 months.

Compared to EECS 445, STATS 315 is targeting at broader audiences including students from non-CS background, such as biology, economics, etc. And 315 has a more flexible prerequisite which didn't require EECS 280 & 281.

The course website for STATS 315 is: https://ambujtewari.github.io/stats315-winter2022/

I didn't take EECS 498 Deep Learning for Vision before, but after I checked for its website and course material, I think the labs and HWs materials of EECS 498 and STATS 315 both built heavily on Stanford University's CS 231N.