This lecture covers the concept of Support Vector Machines (SVM) in machine learning. It explains how SVM works, the minimum number of support vectors required for linear SVM, and the uniqueness of solutions. The lecture also delves into the impact of kernel width on multi-class SVM and decision boundaries.