Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.
Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.
Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.
Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.
Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Covers the probabilistic model for linear regression and the importance of regularization techniques.
Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.
Introduces simple linear regression, properties of residuals, variance decomposition, and the coefficient of determination in the context of Okun's law.
Explores the IRLS algorithm for weighted least squares estimation in GLM.
Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.
