Explainable machine learning 1000-319bEML

- Introduction to explainable artificial intelligence, interpretable machine learning and fairness
- Methods for conditional analysis of predictive models: Break-Down method, Break-Down with interactions, SHAP, ASV
- Methods for model analysis by perturbation: LIME method, LORE
- Methods for contenst model analysis and model sensitivity testing: Ceteris Paribus, Partial Dependence, Accumulated Local Methods
- Method for assessing the importance of variables: Variable Importance by Pertmutations, Model Class Reliance
- Fairness and Biases
- Explanations specific to neural networks