Academic Year 2022-2023
The course will start on October 6, 2022. Teams link.
Instructors: Prof. Battista Biggio
Teaching Assistants: Dr. Maura Pintor, Dr. Ambra Demontis
External Seminars: Dr. Luca Demetrio, Prof. Fabio Roli
MSc in Computer Engineering, Cybersecurity and Artificial Intelligence (Univ. Cagliari)
National PhD Program in Artificial Intelligence
PhD Program in Electronic and Computer Engineering (Univ. Cagliari)
GitHub repository for course material: https://github.com/unica-mlsec/mlsec
Lectures
- Tuesday, 15-18, room N_3B (Lidia), building N
- Thursday, 12-14, room I_IC (ex BI), building I
Course objectives and outcome
Objectives
The objective of this course is to provide students with the fundamental elements of machine learning security in the context of different application domains. The main concepts and methods of adversarial machine learning are presented, from threat modeling to attacks and defenses, as well as basic methods to properly evaluate adversarial robustness of a machine learning model against different attacks.
Outcome
An understanding of fundamental concepts and methods of machine learning security and its applications. An ability to analyse and evaluate attacks and defenses in the context of application-specific domains. An ability to design and evaluate robust machine learning models with Python and test them on benchmark data sets.