Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
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.
Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.
Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
