Introduces linear regression basics from an empirical risk minimization perspective, covering the square loss, data preprocessing, and gradient computation.
Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.
