Covers Generalized Linear Models, likelihood, deviance, link functions, sampling methods, Poisson regression, over-dispersion, and alternative regression models.
Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Introduces logistic regression for binary classification and explores multiclass classification using OvA and OvO strategies.
Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.
