Data Representation: BoW and Imbalanced Data

This lecture covers the concepts of overfitting vs underfitting, model selection, validation set method, LOOCV, k-fold cross-validation, penalizing overfitting, regularized linear regression, kernel ridge regression, and finding the right regularization strength. It also discusses data representation, data normalization, missing data, noisy data, cleaning methods, and the challenges of imbalanced data in machine learning.