Linear Models and Overfitting

This lecture delves into linear models, focusing on solving them and understanding overfitting. The instructor explains the concept of convex functions, the automatic condition for global optimality, and the normal equations for linear regression. By expanding features, the lecture demonstrates how to create more powerful models and discusses the trade-off between model complexity and overfitting. The importance of adding more data to reduce overfitting is also highlighted, along with the computational cost implications. The lecture concludes by emphasizing the significance of model selection in addressing overfitting.