General Information
SSD: ING-INF/05
CFU: 6
Lecturer: Eliana Pastor
Teaching Staff: Gabriele Ciravegna, Eleonora Poeta
⏰ Schedule
- Thursday, 16:00-19:00 – classroom 14
- Friday, 8:30-10:00 – classroom 2I
📚 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)
- Evaluation of explanations (slides)
- Concept-based Explainable AI – Part I (slides), Part II (slides)
- Introduction to NLP (pdf)
💻 Laboratory Material
- Lab 0 – ML Pipeline Review: Scikit-learn & PyTorch (zip)
- Lab 1 – Interpretable by Design Models (zip) (solution) (pdf_solution)
- Lab 2 – Post-hoc global explanation methods (zip) (solution) (pdf_solution)
- Lab 3a – Post-hoc local explanation methods – LIME (zip)
- Lab 3B – Post-hoc local explanation methods – SHAP (zip) (solution) (pdf_solution)
- Lab 4 – XAI for Image data (zip)(solution)
- Lab 5 – Concept-based XAI (zip)
Project