Bias-Variance Trade-Off

This lecture covers the concepts of underfitting and overfitting in machine learning models, explaining how hyperparameters control model flexibility. It delves into error decomposition, polynomial regression, and K Nearest-Neighbors, illustrating the bias-variance trade-off. The instructor discusses the impact of model flexibility on training and test errors, emphasizing the importance of finding the right balance to avoid underfitting or overfitting.