Supervised Maximum Likelihood Learning
		• Finding a set of weights, W, that minimizes the squared errors is exactly the same as finding a W that maximizes the log probability that the model would produce the desired outputs on all the training cases. – We implicitly assume that zero-mean Gaussian noise is added to the model’s actual output. – We do not need to know the variance of the noise because we are assuming it’s the same in all cases. So it just scales the squared error.