About
This course covers explainable artificial intelligence methodologies and techniques for effective model building. The goal is to leverage explainability design principles to build powerful, complex, and transparent models. Students learn methodologies, parametric models, nonparametric models, deep learning complexity, activation and saliency maps, attention and transformer, compliance and ethics, and applications.