Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.
Covers overfitting, regularization, and cross-validation in machine learning, exploring polynomial curve fitting, feature expansion, kernel functions, and model selection.
Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
Covers ANOVA method, focusing on partitioning total sum of squares into treatment and error components, mean square calculations, Fisher statistic, and F-distribution.
Introduces the FIN-403 Econometrics course, emphasizing practical application of standard econometric models like Ordinary Least Squares (OLS) in economic and financial contexts.
Explores challenges and solutions for scalable and trustworthy learning in heterogeneous networks, emphasizing data heterogeneity, privacy, fairness, and robustness.
