The document discusses support vector machines (SVMs), a type of machine learning algorithm used for classification and regression. It describes how SVMs find the optimal separating hyperplane between two classes of data by maximizing the margin between the closest data points of each class. This is achieved by formulating the problem as a quadratic programming optimization with inequality constraints to minimize errors. The document also addresses how SVMs can be adapted to handle noisy data using slack variables that allow some misclassifications with a penalty determined by a tradeoff parameter.