Support Vector Machines: Theory and Applications

This lecture covers the theory and applications of Support Vector Machines (SVMs) in machine learning. It explains Vapnik's invention of a training algorithm for optimal margin classifiers, the concept of support-vector networks, linear classifiers, hard-SVM rules for max-margin separating hyperplanes, soft SVMs for non-linearly separable data, classification by risk minimization, convex relaxation of classification risk, losses for classification, and the application of convex duality to SVMs. The lecture also delves into optimization techniques for finding the optimal hyperplane, convex duality in SVMs, and the interpretation of the dual formulation in SVMs.