* overview of the concepts behind the linear and non-linear SVM
* feature representations and kernels. second order feature representation and the second order polynomial kernel in detail; semi-positive definiteness of kernels; polynomial kernel, and properties of the Gaussian kernel. 
* derivation of the dual optimization of the SVM with slack variables.