CODE 108857 ACADEMIC YEAR 2026/2027 CREDITS 4 cfu anno 2 ROBOTICS ENGINEERING 11963 (LM-32) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR ING-INF/05 LANGUAGE English TEACHING LOCATION GENOVA MODULES Questo insegnamento è composto da: TRUSTWORTHY AI ROBOTICS USE CASES TRUSTWORTHY ARTIFICIAL INTELLIGENCE TEACHING MATERIALS AULAWEB OVERVIEW Today machine-learning algorithms and AI-based systems are used for many real-world applications, including image recognition, spam filtering, malware detection, biometric recognition. In these applications, the learning algorithm may have to face intelligent and adaptive attackers who can carefully manipulate data to purposely subvert both the learning and the operational phases. Part 1 of the course aims to introduce the fundamentals of the security of machine learning and the related field of adversarial machine learning. Part 2 introduces the international regulations behind the so called “trustworthy AI. The course uses application examples including object recognition in images, biometric recognition, robotics. AIMS AND CONTENT LEARNING OUTCOMES The course is composed of two modules, each focusing on the security and reliability of AI systems in robotics. Overall, the course aims to provide students with both theoretical and practical expertise in ensuring the security, safety, and trustworthiness of AI systems used in robotics. PREREQUISITES This course is for graduate students who already attended basic courses (or have a basic/intermediate knowledge) of machine learning and artificial intelligence and have a basic/intermediate knowledge of programming languages (in particular, the Python language). TEACHING METHODS Students who hold valid certificates relating to Specific Learning Difficulties (SLD), disabilities or other educational needs are invited to contact the lecturer and the school’s disability liaison officer at the start of the course to agree on any teaching arrangements which, whilst respecting the course objectives, take into account individual learning styles. The contact details for the university’s disability liaison officer are available at the following link: https://unige.it/commissioni/comitatoperlinclusionedeglistudenticondisabilita. TEACHERS AND EXAM BOARD FABIO ROLI Ricevimento: Contact the instructor by email. www.saiferlab.ai/people/fabioroli LUCA DEMETRIO Ricevimento: Contact the instructor by email to schedule an appointment. Exam Board FABIO ROLI (President) LESSONS LESSONS START https://easyacademy.unige.it/portalestudenti/index.php?view=easycourse&_lang=it&include=corso Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION Intermediate in class assignments, or home assignmemt and oral exam. ASSESSMENT METHODS Solutions of open and closed questions, numerical excercies (15/309 + coding exercises (15/30). FURTHER INFORMATION Students with valid certifications for Specific Learning Disorders (SLD) may request accommodations for exams at least 7 days prior to the exam date by filling out the “accommodation request form” (available via online services at https://modulionline.unige.it/richiesta-adattamenti# no-back), which will be automatically forwarded by the system to the instructor in charge of the course and to the faculty liaison for students with disabilities and SLDs in their School/Department. The student will receive a copy of their request.
