Fairness and discrimination, PhD Course, #3 Machine Learning, losses and distances

For the third course, we will get back a little bit on machine learning (slides are still online on the github repository). The starting point will be loss functions and risk. Loss functions and risk A general definition for a loss is that it is positive, and null when we consider . As we will discuss further, it is neither a distance, nor a dissimilarity measure Then, define the empirical risk (and the associated empirical risk minimization principle, as coined in Vapnik (1991)) Given … Continue reading <span …