Temple University 3 credits ECE
This course covers explainable artificial intelligence methodologies and engineering techniques for effective model building. The goal is to leverage explainability design principles to build and engineer powerful, complex, and transparent models. Students learn methodologies, parametric models, non-parametric models, deep learning complexity, activation and saliency maps, attention and transformer, and applications.
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