Cross-Validation: Techniques and Applications

This lecture covers the concept of cross-validation, including k-fold cross-validation and leave-one-out methods. It explains how cross-validation helps in model selection and hyper-parameter tuning. The lecture also discusses overfitting with linear models, regularization techniques, and their application in linear regression and logistic regression. Additionally, it explores multi-output ridge regression, kernel ridge regression, and the incorporation of regularization in support vector machines. Practical examples and exercises are provided to reinforce the theoretical concepts.