Lecture
This lecture covers model evaluation in machine learning, focusing on model assessment and selection, theory behind expected loss, training error, and prediction error. It also discusses resampling methods like cross-validation and bootstrapping, as well as information criteria such as AIC and BIC. The instructor emphasizes the importance of comparing model performance to baseline models and quantifying uncertainty in performance metrics.