Consider a regression problem involving multiple target variables in which it is assumed that the distribution of the targets, conditioned on the input vector x, is a Gaussian of the form p(t|x,w) = N(t|y(x,w),?) where y(x,w) is the output of a neural network with input vector x and weight vector w, and ? is the covariance of the assumed Gaussian noise on the targets. Given a set of independent observations of x and t, write down the error function that must be minimized in order to find the maximum likelihood solution for w, if we assume that ? is fixed and known.

Consider a regression problem involving multiple target variables in which it is assumed that the distribution of the targets, conditioned on the input vector x, is a Gaussian of the form p(tlx, w)-N(tly(x, w). 2) where y(x, w) is the output of a neural network with input vector x and weight vector w, and ? is the covariance of the assumed Gaussian noise on the targets. Given a set of independent observations of x and t, write down the error function that must be minimized in order to find the maximum likelihood solution for w, if we assume that E is fixed and known. Consider a regression problem involving multiple target variables in which it is assumed that the distribution of the targets, conditioned on the input vector x, is a Gaussian of the form p(tlx, w)-N(tly(x, w). 2) where y(x, w) is the output of a neural network with input vector x and weight vector w, and ? is the covariance of the assumed Gaussian noise on the targets. Given a set of independent observations of x and t, write down the error function that must be minimized in order to find the maximum likelihood solution for w, if we assume that E is fixed and known