I've recently embarked on a project to teach myself some machine learning basics. Since my background is in Radial Basis Function (RBF) interpolation and numerical methods for PDEs, it seems natural to start with RBF neural networks (RBF-NN) and work outward from there.
There are many great tutorials online, and mine aren't going to be very special. However, they will contain the details that I needed to get things up and running, and will eventually include code also. I will also be applying some of the state-of-the-art technology from the RBF interpolation world to the RBF-NN world.
At some point, I will move on from RBF-NN into SVM, deep learning, and so forth. Let's go.