In this talk, principles and intuitions behind support vector machines will be exposed.
We will see what maximum margin is, why it is pursued and how it is achieved. We will 
see also the unavoidable mathematical details of a SVM and a very intuitive geometrical
intrepretation of them. Next we will see how to solve non-linearly separable problems 
using the kernel trick. This will allow us to understand mathematically and geometrically 
what the hell a kernel is. With this knowledge we will learn the conditions a measure 
must fulfill in order to be used as a kernel.  Finally, we will present a successful 
semi-supervised approach to SVMs: Transduction. We will see the foundations of 
transduction, successful applications, why it is hard problem and approaches to 
approximate a solution to the transduction problem.