Discusses kernel methods in machine learning, focusing on kernel regression and support vector machines, including their formulations and applications.
Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.
Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
Introduces the FIN-403 Econometrics course, emphasizing practical application of standard econometric models like Ordinary Least Squares (OLS) in economic and financial contexts.
Covers ANOVA method, focusing on partitioning total sum of squares into treatment and error components, mean square calculations, Fisher statistic, and F-distribution.
