19
Explainable AI
SHAP, LIME, feature importance, and model interpretability
4 lessons180 min totaladvanced
1
Feature Importance
Permutation importance, partial dependence plots (PDP), individual conditional expectation (ICE), feature interaction, and built-in vs model-agnostic importance
2 exercisesQuiz40m
2
SHAP & LIME
SHAP theory (Shapley values from game theory), TreeSHAP, KernelSHAP, DeepSHAP, LIME (local linear approximations), anchors, and when to use each
2 exercisesQuiz50m
3
Model Debugging
Error analysis, slice-based evaluation, adversarial examples, robustness testing, model calibration (Platt scaling, isotonic), and uncertainty estimation (MC Dropout, ensembles)
2 exercisesQuiz45m
4
Bias & Fairness
Types of bias (selection, measurement, algorithmic), fairness metrics (demographic parity, equalized odds, calibration), bias detection (AIF360, Fairlearn), and mitigation strategies (pre/in/post-processing)
2 exercisesQuiz45m