Lecture 15: Bayesian Interpretations of Regularization
Charlie Frogner


We cover probabilistic interpretations of Tikhonov regularization -- specifically, regularized least squares. We show how ERM, linear RLS and kernel RLS can be derived in a Bayesian framework and discuss implications and possible limitations.


Slides for this lecture: PDF

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