Networks for Learning: Regression and Classification

The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass cl…

The syllabus, on MIT OpenCourseWare

The full course — syllabus, assigned readings, problem sets, exams, and lecture notes — lives on OCW. These open the real thing: