When is minimizing the squared error equivalent to Maximum Likelihood Learning?

• Minimizing the squared residuals is equivalent to maximizing the log probability of the correct answer under a Gaussian centered at the model’s guess.

t = the correct answer

y = model’s estimate of most probable value

can be ignored if sigma is same for every case

can be ignored if sigma is fixed