> My one complaint is that the programming assignments weren't interesting at all. The results were interesting, but the setups were mostly given to us, and we just had to code an algorithm that was in our notes. For someone who understands the basics of linear algebra and programming, it was just a syntax challenge, and that got irritating after a bit so I stopped doing them.

I agree with this. The programming assignments I've done so far in the Machine Learning class are usually 5-7 matlab functions, many of which are about 2 lines of code (the longer ones might be ~10 lines of code). If you've ever done matlab/octave programming the assignments will take about 20-30 minutes and be completely unenlightening as you're literally just translating mathematical notation into matlab (which is, by design, already a lot like mathematical notation anyway). They provide entirely way too much skeleton code to learn anything from if you're not actively trying to learn. If I weren't already mostly familiar with most of the material presented in the class, I imagine I would never retain knowledge of how the machine learning "pipeline" worked or have any high-level understanding of the algorithms, because the assignments just require you to implement the mathematical pieces of each step, without ever asking you to, for example, actually call any optimization routines, or put the pipeline together.

The problem, I think, is that it would just be too difficult to do automatic grading in a way that is reasonably possible to pass if they don't turn most of the work into skeleton code. Since the automatic grading needs nearly exactly matching results, one minor implementation difference in a perfectly good implementation of the algorithm itself (e.g., picking a single parameter incorrectly, picking the optimization termination conditions incorrectly, choosing a different train/dev split, etc.) would make the entire solution completely wrong.