Support Vector Machines: Basics and Applications

This lecture introduces Support Vector Machines (SVM), covering topics such as linear separability, hyperplanes, margins, hard-margin SVM, soft-margin SVM, and non-linear SVM with kernels. It also discusses the history of SVM, its development by Vapnik and Lerner, and its extension to non-linear models. The slides illustrate the concept of finding hyperplanes to separate classes, the use of kernels for non-linear separations, and the mathematical definition of linear separability. References to key researchers in the field are provided, along with additional resources for further reading.