General Information
SSD: ING-INF/05
CFU: 6
Lecturer: Eliana Pastor
Teaching Staff: Elena Baralis, Gabriele Ciravegna, Salvatore Greco, Eleonora Poeta
Schedule
- Monday, 8:30-10:00 – classroom 12I
- Thursday, 8:30-10:00 – classroom 7D
- Friday, 8:30-10:00 – classroom 11I
Teaching material
- Course introduction (slides)
- Trustworthy AI: definition and motivations (slides)
- Explainable AI: taxonomy (slides)
- Pre-modeling explainability (slides)
- In-modeling explainability (slides)*
- Post-hoc model agnostic – global (slides)
- Post-hoc model agnostic – local surrogate models (slides)
- Post-hoc model agnostic – local – explaining by removing/perturbing (slides)
- Post-hoc (model agnostic) – local – gradient-based explanation methods (slides)
- Concept-based Explainable AI – part I (slides)
Laboratory material
- Lab 0.1: Machine Learning pipeline with Pandas and Scikit-learn (zip) (zip_solution) (pdf_solution)
- Lab 0.2: Introduction to PyTorch with Deep Learning (zip) (zip_solution) (pdf_solution)
- Lab 1: Interpretable by design (zip) (zip_solution) (pdf_solution)
- Lab 2: Post-hoc global explanation methods (zip) (zip_solution)
- Lab 3a: Post-hoc Local explanation methods – LIME (zip) (zip_solution) (pdf_solution)
- Lab 3b: Post-hoc Local explanation methods – SHAP (zip)
- Lab 4a: Image data Analysis & Saliency Maps (zip) (zip_solution) (pdf_solution)
- Lab 4b: XAI for Image data (zip)
The material is also available in Github.