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
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Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
- Bachelor's degree, first cycle programme, Computer Science
- Master's degree, second cycle programme, Computer Science
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: