Optimal Regularization Strength and Learning Curves

This lecture provides feedback on Homework 1, focusing on loading datasets, understanding dimensions, and dividing data into training, test, and validation sets. The instructor explains the importance of extrapolating information from datasets and the significance of normalizing data. The lecture covers learning curves, test errors, and the impact of regularization on overfitting. Students are guided on computing optimal regularization strength and interpreting regularization paths. The differences between L1 and L2 regularization are discussed, emphasizing feature selection and the effect on test errors with varying training set sizes.