Correlation quantifies the relation between features. Linear correlation methods are robust and computationally efficient but detect only linear dependencies. Nonlinear correlation methods are able to detect nonlinear dependencies but need to be carefully parametrized. As an example for nonlinear correlation we present the chi-square test for independence that can be applied to continuous features using histogram counts. Nonlinear correlation can also be quantified by the cross-validation error of regression models. Correlation does not imply causality. Spurious correlations may lead to wrong conclusions. If the underlying features are known, then spurious correlations may be compensated by partial correlation methods.