Support Vector Machines: Theory and Applications

This lecture covers the theory and applications of Support Vector Machines (SVM) in machine learning. It discusses the concept of SVM, the minimum number of parameters required for prediction, the uniqueness of solutions, the role of Support Vectors, and the impact of adding more data points on the SVM model. The lecture also explores non-linear classification using SVM, the use of RBF kernel for decision boundary construction, and multi-class SVM classification. Various scenarios are presented to understand the behavior of SVM in different situations.