Regularization in Machine Learning

This lecture covers the concepts of Ridge Regression and Lasso Regression, focusing on regularization techniques to prevent overfitting in machine learning models. It explains how regularization impacts model flexibility and parameter values. The examples demonstrate the application of regularization in Julia code, emphasizing the importance of tuning hyperparameters and interpreting the results. Additionally, it discusses the use of Lasso paths to visualize the effect of different regularization values on parameter coefficients. The lecture concludes with practical exercises on implementing and understanding regularization in machine learning models.