Model Selection: AIC and BIC

This lecture covers the concepts of model selection using AIC and BIC criteria, comparing their applications and discussing the Bayesian approach to model selection. It explains how AIC and BIC address different questions related to selecting the best model and the importance of sparsity in model selection. Additionally, it introduces the Generalized Information Criterion (GIC) and its connection to penalization methods for model selection.