Chapter 12 presents discriminant analysis – a classical (and powerful) supervised learning approach for classification. Discussed are Fisher’s discriminant analysis, as well as Gaussian linear, quadratic, and regularized discriminant analysis. The chapter concludes with a discussion of partial least squares discriminant analysis, which is still popular in some application areas, even if its application to high-dimensional data is likely to result in solutions that are suboptimal in terms of predictive abilities and interpretability (alternative approaches are recommended).