SVMs and Feature Maps

This lecture delves into the concept of Support Vector Machines (SVMs) and the introduction to feature maps. The instructor explains the logistic loss and hinge loss for classification problems, emphasizing the importance of finding the maximum margin solution. By transforming data into higher-dimensional spaces using feature maps, SVMs can handle non-linearly separable data. The lecture also covers the bias-variance tradeoff, regularization, and the significance of choosing the right feature map. Additionally, the instructor introduces the kernel method and functional space method as alternative approaches to understanding SVMs in higher dimensions.