Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.
Explores the geometric explanation of why Lasso solutions are sparse and how coefficients change with the regularization parameter.
Introduces simple linear regression, properties of residuals, variance decomposition, and the coefficient of determination in the context of Okun's law.
Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.
