Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers linear regression, regularization, inverse problems, X-ray tomography, image reconstruction, data inference, and detector intensity.
Explores linear regression, least squares, residuals, and confidence intervals in regression models.
Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
