Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.
