Generalized linear model Gaussian distribution Linear Model Is a generalized linear model with a Gaussian distribution the same as a linear model?
 A: Essentially yes, if you think about a gaussian generalized linear model (glm) with identity link function, then that is the same as what is usually meant with a linear model. 
But, "linear model" could be used with other shades of meaning, like estimating it with some other loss function than sum of squares. And a gaussian glm could use some other link function than the identity.
A: In the generalized linear model (specially meant for classification problem), the source of non-linearity is activation funtion with linear argument of the form $\omega^\top x +\omega_0$. Here, we just need to estimate the parameters $\omega, \omega_0$ only and the activation might be user defined.
While, in regression problem, the using least square estimation, the prediction function happens to be a conditional expectation of the form $E(y|x)$. If the joint distribution of x and y is Gaussian, then the prediction function $E(y|x)=\omega_0 + \omega^\top x$ linear both in the parameters $\omega, \ \omega_0$ and in the predictor x.
