Support Vector Machines: Maximizing Margins and Linear Classifiers - Lecture 19

This lecture continues the exploration of Support Vector Machines (SVMs) in machine learning, examining the relationship between maximizing margins and learning linear classifiers. Discover how this connection leads to the SVM objective function, which introduces the fundamental concept of regularized risk minimization. The 1 hour 20 minute session from UofU Data Science provides essential insights for understanding this powerful classification algorithm. For additional resources and supplementary materials, visit the lecture's dedicated webpage which contains comprehensive notes and references.