The document discusses flexible discriminant analysis (FDA), which is a machine learning technique for classification. FDA works by mapping data into a high-dimensional space and finding linear combinations of variables that best separate the classes of data. It allows nonlinear decision boundaries unlike traditional linear discriminant analysis. The summary discusses how FDA can be used for classification tasks like identifying bird species based on skeletal features.