Supervised Learning EssentialsIntroduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Nonlinear ML AlgorithmsIntroduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Linear and Logistic RegressionIntroduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.
Red bus/Blue bus paradoxExplores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Linear Models: ClassificationExplores linear models for classification, including logistic regression, decision boundaries, and support vector machines.
