[srean]

> … you need tools from convex optimization. So it doesn't feel appropriate to teach it in a standard machine learning class.

Those topics were perquisites to taking ML at the grad level when I took them. You either had to have relevant courses in your bag or convince the prof that you could handle it.

[eachro]

Yea grad level absolutely (pretty much anything can fly at the grad level). Undergrad? Maybe we should teach it b/c of the historical importance it has to the field and how the community developed but I really do think most ML classes would be better off without it b/c of the extra background you'd have to use precious time on. Kernel PCA, kernel regression are better for demonstrating the power of kernels.

I suppose the idea of a maximum separating hyperplane is kind of unique to SVMs and if you just teach SVMs through the primal and leave it at that, you don't need to spend all that much time motivating the dual.