Explainable AI. 3 credits, 3 contact hours

Catalog description

Prerequisites: CS 370 and CS 375 with a grade of C or better. This course introduces technical methods for making machine learning models more transparent and understandable. Topics include intrinsically interpretable models, post hoc explanations (e.g., Shapley values, saliency maps), visualization of model internals (e.g., attention maps, neuron activations), surrogate modeling, mechanistic interpretability, and communication bottlenecks. Visualization is a central theme throughout the course, both as a practical tool and as a key research frontier. CS 477 is recommended but not required.