Support Vector Machines

Abstract: This seminar provides an intuitive introduction to Support Vector Machines (SVMs). We begin with the maximal margin classifier and support vector classifier, building geometric intuition for how SVMs separate classes with optimal margins. We then extend these ideas to the kernel trick, enabling highly flexible nonlinear decision boundaries through polynomial and radial basis function kernels. The talk also highlights key tuning parameters, practical considerations for model fitting, and strategies for avoiding overfitting.

Computer scienceMathematics

Audience: general audience

Basic Notions and Applied Topology Seminar