The course gives a comprehensive introduction to the problems of pattern analysis and the kernel methods approach to their solution. Kernel methods rely on the implementation of linear pattern functions in high dimensional feature spaces defined implicitly via a kernel function. The course will cover the statistical implications, algorithmic solutions and kernel design strategies that make this approach a modular and flexible way to tackle real-world tasks.