Data Representations & Processing

This lecture covers data representations and processing, including overfitting, model selection, cross-validation, regularization, kernel ridge regression, and finding the right regularization strength. It also discusses the Bag of Words model, data normalization, cleaning noisy data, and learning with imbalanced data. The instructor explains sampling methods, empirical risk, sample re-weighting, and transitioning from handcrafted representations to learned ones.