Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Explores statistical estimation, regression models, and model selection in parametric models.
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Explores estimation, detection, linear estimators, and the orthogonality principle in machine learning.
Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.
Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
