Covers quantile regression, focusing on linear optimization for predicting outputs and discussing sensitivity to outliers, problem formulation, and practical implementation.
Explores the formulation and complexity of Support Vector Machines, including primal and dual forms, geometric interpretation, and algorithmic implications.