📖 Course Overview
The course is a module of NNDL course. It focuses on topics and challenges related to trustworthiness in specific cyber-physical contexts, such as driving and autonomous systems. It also explores how to make neural networks more interpretable, robust, and secure.
The main topics of the course are organized as follows:
- Explainable and Interpretable AI
- Anomaly and Out-of-Distribution Detection Methods
- Domain Generalization and Domain Adaptation
- Adversarial Robustness and Defense Methods
- Simulation and Tools for Trustworthy AI
- Functional Components in Autonomous Driving
🛠️ Format & Exam
- Lectures: 20 hours (remote).
- Exam (2 CFU): Project/Research work + oral discussion.
TBD (available soon)
🗓️ Schedule
TBD (available soon)
📂 Lectures
You can find all lectures and notebooks in the following OneDrive folder: LINK
The folder is password-protected. Attendees can contact me directly to request access.
📬 Contact
giulio.rossolini@santannapisa.it