theta you get some output Y hat and along with the X you also have a y:i true label and then you have a loss function that compares what you output with your true label and then you just try to make that loss smaller you want to adjust your parameters so next time you see data point X its output will be a little closer to the true label Y and we call this empirical miss empirical risk minimization because you don't actually what you think is that your X comes from some distribution from some data distribution D like the space of