In this talk we apply Statistical Learning techniques based on the Gaussian distribution to solve problems arising in Quantitative Finance. In particular we apply Gaussian Mixture models and Gaussian Process Regression to data driven, model agnostic Pricing/Hedging of European, Bermudan or American Options with Vanilla or Exotic payoffs, Yield Curve building, exposure calculation, or simulation of synthetic data. The idea of using Statistical Learning methods based on Gaussian distributions is to bridge the gap between innovation and regulatory pressure that is prevalent in the Financial Markets. The latter makes it hard to apply state-of-the-art techniques in the field to the quantitative risk management for example. We suggest to make the methods more applicable by using simple building blogs and well known concepts. The examples we present are used to illustrate that complex tasks and simple building blocks are no contradiction.