Nonlinear SVM: Kernels and Dual Optimization

This lecture covers the concept of transforming data into a higher-dimensional feature space using nonlinear maps to make the problem linear, introducing the idea of kernels to compute distances in the feature space, and explaining the dual optimization of SVM with kernels. It also discusses how to interpret the decision function and visualize the results of SVM.