Linear Regression BasicsCovers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Linear RegressionCovers linear regression for estimating train speed using least squares and regularization.
Kernel Methods: Machine LearningExplores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.
Linear & Weighted RegressionCovers linear and weighted regression, limitations, and introduces locally weighted regression for model-free regression.
Nonlinear ML AlgorithmsIntroduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
