Lecture
This lecture covers the basics of nonlinear machine learning algorithms, starting with a recap of the simplest nonlinear algorithm and moving on to nearest neighbor and k-nearest neighbors methods. It also discusses polynomial curve fitting, feature expansion, and the concept of model complexity and overfitting. The instructor explains the importance of cross-validation in model selection and demonstrates the use of regularization in linear regression and logistic regression. The lecture concludes with a discussion on incorporating regularization into SVM and finding the right regularization strength.