Kernel Methods: Recap and Applications

This lecture provides a recap on polynomial feature expansion, kernel functions, the representer theorem, kernel regression, kernel ridge regression, kernel SVM, and prediction methods. It also covers the importance of data representations, normalization techniques, and dealing with imbalanced data in machine learning.