Select, test, and communicate model explanations without overstating what they prove.
Explore the distinction between interpretability and post-hoc explanations, examine feature attribution, and consider interpretable neural models and production constraints. Learners test explanation stability, check whether a method reflects model behavior, and communicate an explanation's scope to the people who need it. The course pairs English-language Creative Commons videos with original analytical notes and assignments.
Distinguish an interpretable model from an explanation applied after a model has been trained. Explain the assumptions and limitations of feature-attribution methods such as SHAP. Evaluate explanation stability, audience fit, and decision risk before communicating an explanation.
Prior experience with supervised machine learning and model evaluation is recommended. Basic familiarity with features, predictions, and classification or regression will support the activities.
Experienced learners who want to deepen and apply advanced AI skills
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
4 lessons · Advanced · Full course.
This course uses CC BY licensed material attributed to its content provider or uploader. Lesson playback is available only through authenticated access after publication and licensing checks pass.