Lecture
This lecture covers the concepts of polynomial regression and gradient descent. It starts by explaining the process of polynomial regression, where the feature space is expanded to include polynomial terms. The lecture then delves into the gradient descent algorithm, a method for optimizing functions iteratively. It discusses the challenges of overfitting and underfitting in regression models and introduces regularization techniques like Lasso regression to prevent overfitting. The importance of feature scaling for convergence in optimization algorithms is also highlighted. The lecture concludes with a discussion on the iterative nature of gradient descent and the need for multiple runs to find global minima in non-convex functions.