Today's lecture in my machine learning class was on artificial neural networks, slides below:
The approach to introduce them was to consider them just a way of automatically learning basis functions in a linear regression setup.
While this isn't really the full story, it is motivated by the project they have just handed in, where they needed to predict values based on trained linear regression models.
Training linear models is rather straightforward, but guessing good feature functions (transformation of the predictor variables) is tricky, and for the data I gave them in the project, some of the models were downright evil.
This should motivate having models where you don't need to be able to guess the features -- or at least where it isn't as essential -- and that is how I present neural networks.
I think I'll give my students another project now, that is just re-doing the first project but using neural networks instead of linear regression...