We want a hypothesis that is bounded between zero and one, regression hypothesis line extends beyond this limits. Hypothesis here also represents probability of observing an outcome.
Hypothesis by ISLR and Andrew N.G :
Odds and log-odds/logit
In regression beta1 given average change in y for unit change in x. But here it says unit increase in x changes log-odds by beta1. It multiplies odds by exp(beta1) and hence depends on current value of odds and therefor is not linear.
For ISLR perspective it is likelihood that we want to maximize.
Andre N.G looks it from the perspective of modifying cost function of linear regression.
As we can see that Andrew N.G cost function is same as maximizing log likelihood of ISLR.
Least square in case of linear regression is special case of maximum likelihood. We know that derivation where we assume likelihood to be gaussian.