Bias-Variance Tradeoff in Machine Learning

This lecture explores the Bias-Variance tradeoff in machine learning, focusing on how the risk changes with the complexity of the model class. Through a series of experiments and error decompositions, the instructor explains the concepts of bias, variance, and noise in predictive models. The goal is to find a balance between bias and variance to minimize the true error. The lecture also delves into the challenges of underfitting and overfitting, emphasizing the importance of selecting models with both low bias and low variance. The discussion concludes with insights on the double descent curve and the reconciliation of modern machine learning practices with traditional bias-variance principles.