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Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.
Covers linear models, binary and multi-class classification, and logistic regression with practical examples.
Covers logistic regression for linear classification and unsupervised dimensionality reduction techniques.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Explores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.
Introduces logistic regression for binary classification and explores multiclass classification using OvA and OvO strategies.
Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.
