Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.
Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.
Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
