Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
Covers the basics of linear regression in machine learning, exploring its applications in predicting outcomes like birth weight and analyzing relationships between variables.
Introduces the Generalized Method of Moments (GMM), a versatile approach for estimation based on moment restrictions, with applications in asset pricing models.