Machine Learning, Deep Learning, Algorithms, Loss Function, Optimization.
output of the linear regression on the i-th input x^(i) and m is the number of the training examples. We see that first two terms are constant so basically maximizing the log-likelihood means minimizing the MSE as: We immediately see that maximizing the log-likelihood with respect to θ yields the