Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Covers Generalized Linear Models, likelihood, deviance, link functions, sampling methods, Poisson regression, over-dispersion, and alternative regression models.
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
