Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.
Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Introduces simple linear regression, properties of residuals, variance decomposition, and the coefficient of determination in the context of Okun's law.
Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.
