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Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.
Explores convex optimization, convex functions, and their properties, including strict convexity and strong convexity, as well as different types of convex functions like linear affine functions and norms.
Explores linear regression fundamentals, non-linear regression issues, and R-squared goodness of fit, with examples like Anscombe's quartet and the Datasaurus dataset.
Explores spatial regression models, addressing spatial autocorrelation challenges and the concept of spatial lag models to correct biases and improve inference accuracy.
Introduces the fundamentals of regression in machine learning, covering course logistics, key concepts, and the importance of loss functions in model evaluation.
