Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.
Explores linear regression, least squares, residuals, and confidence intervals in regression models.
Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.
