Data Representations & Processing

This lecture covers the importance of data representations and processing in machine learning, focusing on topics such as overfitting, model selection, cross-validation methods, penalizing overfitting, regularized linear regression, kernel ridge regression, and finding the right regularization strength. It also delves into the concepts of bag of words, visual dictionaries, histograms, and data normalization techniques. The lecture emphasizes the challenges posed by imbalanced data and explores solutions like sampling methods, sample re-weighting, and the transition from handcrafted representations to learned ones.