Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.
Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
