The course will introduce the students to techniques in of explainable Artificial Intelligence and machine learning, ranging from interpretable-by-design models to post-hoc explanations. It will also introduce methods to measure and enforce reliability of machine learning models, including verification and methods to build robust predictors. Students will learn which are the best techniques to provide explanations and how to assess and enforce reliability depending on data and criticality of applications.
Explainable and Reliable Artificial Intelligence
Learning Goals
Program in pills
Introduction to Explainable AI. Transparent-by-design models: rule based and logic based models. Post-hoc explainability: feature importance, LIME, SHAP, relevance propagation and gradient based methods, global explanation approaches. Reliable AI: adversarial training, robustness of bayesian models, verification of ML models and neural networks.i
Area
Machine Learning and Artificial Intelligence
Curriculum Foundations
TAF Type
Curriculum Industry
TAF Type
Curriculum Health
TAF Type
Curriculum Economy
TAF Type
SSD
ECTS
Semester
Lecturers
Luca Bortolussi
Tatjana Petrov