Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.
Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
