Question: why mle for logistic regression?
Answer: In logistic regression, σ(z) turns an arbitrary “score” z into a number between 0 and 1 that is interpreted as a probability. Positive numbers become high probabilities; negative numbers become low ones. In order to chose values for the parameters of logistic regression, we use maximum likelihood estimation (MLE).