Re: Q14 about linearly separable by SVM
You are right, but in the lectures we did not prove that for the RBF kernel, so it was worth exploring the question at least empirically.
In general, it is conceivable that a transformed infinitedimensional space may still fail to separate a finite data set. For instance, take every dimension in the infinitedimensional space to be a (possibly different) linear transformation of the original space. In this case, you would still be implementing just a linear classifier in the original space when you use a linear classifier in the transformed space, so you will fail to separate a set of points that is not linearly separable in the original space.
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