Linear Regression Optimization

This lecture covers the concept of linear regression, matrix multiplication, the D > N problem, cost functions like Mean Square Error (MSE) and Mean Absolute Error (MAE), and the trade-off between statistical and computational properties. It also discusses convexity, outliers, different optimization algorithms like grid search, and the challenges of optimization landscapes in machine learning.