Statistical Learning Theory
MIT OpenCourseWare's graduate-level course examines why machine learning algorithms generalize from training data to new examples. The syllabus covers Vapnik-Chervonenkis theory, concentration inequalities in product spaces, and empirical process theory, applying these tools to analyze boosting, support vector machines, and neural networks. Materials include lecture notes and problem sets developed for MIT's mathematics department, focused on the theoretical guarantees behind popular learning algorithms rather than their implementation. The course assumes a solid background in probability and real analysis and is aimed at students who want to understand the mathematical foundations of statistical learning rather than just apply the algorithms. As with other MIT OCW offerings, all materials are free to access online under MIT's open license, with no instructor interaction or certificate attached.