DSC 270. Interpretability and Explainability in Machine Learning (4 units)
Link to catalog page: https://catalog.ucsd.edu/courses/DSC.html#dsc270
Description
This course will cover advances in interpretable and explainable machine learning. Topics include modern methods to learn simple, transparent models (e.g., rule lists, decision trees, sparse linear models); methods for post-hoc explainability (e.g., SHAP, LIME, saliency maps, prototype-generation methods); and overarching concepts in the interpretation, description, explanation of machine learning (e.g., model multiplicity, cognitive biases, user studies). Prerequisites: DSC 240. Students should be comfortable reading research papers with a critical perspective; have prior course work in machine learning (e.g., DSC 240); and have some interest in interpretability and explainability for your research or career. Restricted to major codes DS75 and DS76.
Prerequisite courses
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Successor courses
No courses have DSC 270 as a prerequisite.