Lecture
This lecture covers the generalization capabilities of Support Vector Machines (SVM), explaining how SVM can predict class labels even in the absence of data points, the lack of confidence in predictions, and the risk of false positives. It also compares SVM with Gaussian Mixture Models (GMM) and K-Nearest Neighbors (KNN), highlighting the advantages of SVM. The instructor emphasizes the wide range of applications of SVM in various data types and its efficiency in large-scale classification tasks.