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Okay so welcome back and this is going to be the last CS 485/685, University of Waterloo. Mar 11, 2015. All right so okay so the the topic that we're gonna study today i mean we will finish off this part on the tech Dimension i think the greasy dimension appears not just in the So i mean whenever we study a new For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: stanford.io/3E4MouM ... So input output pairs we want to come up with some function And then we define something initially we define something called empirical rather marker complexity or set of function class Professor Shai Ben-David teaching the Foundations of Regularization - Putting the brakes on fitting the noise. Hard and soft constraints. Augmented error and weight decay.
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