Model Selection and Evaluation: Bias-Variance Dilemma, Ridge Estimation

This lecture covers the concepts of over-learning, generalization, and under-learning in machine learning models. It explains the bias-variance tradeoff, approximation errors, and the ridge regression technique. The instructor illustrates how to select the optimal model and discusses the impact of bias and variance on model performance.