Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.
Explores statistical estimation, regression models, and model selection in parametric models.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Explores linear models for classification, including logistic regression, decision boundaries, and support vector machines.
Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
