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.
Covers the analysis of chimpanzee data on learning times and explores generalized linear models and logistic regression.
Explores statistical estimation, regression models, and model selection in parametric models.
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
