Support Vector Machines: Non-Linear Data Mapping

This lecture by the instructor covers the concept of mapping non-linearly separable data to higher dimensions using SVM. Various examples are discussed, including 1D and 2D classification, polynomial approximation, and the use of SVM with a polynomial kernel. The lecture also delves into polynomial feature expansion, least-squares formulation, regularization, and the implications of noise in data. Applications of SVM in rainfall prediction and stock price forecasting are explored. The importance of careful curve-fitting methods and the interpretation of linear decision boundaries in high-dimensional spaces are highlighted.