This class is more or less a high level overview of machine learning. It covers common tools for analysis and feature extraction like dimensionality reduction and goes over common ML models. Some examples here include Naive Bayes, SVMs, decision trees, neural networks, etc. The coursework load is fairly light for an engineering class with 4 projects, which are long and reasonably well-guided assignments. These can be done in a group or alone, where I opted for the latter and would generally recommend that as it is quite doable and you learn more this way than by carving them up.

Regarding the lectures and Prof. Roychowdhury, I generally found them a bit disorganised and did not engage with them much. Roughly 3 lectures in I pretty much focused only on the assignments and was fine. Said assignments I very much enjoyed though, as they were heavy on programming and analysis, which I wanted to practice. They were not particularly mathematically rigorous though, so I would recommend ECE 246 "Foundations of Statistical Machine Learning", by Prof. Diggavi for that.