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

Understanding of fundamental concepts and advanced methods on the security of machine learning and trustworthy artificial intelligence and their applications to pattern recognition. Ability to answer open-ended questions with closed books, solve numerical exercises, use open-source libraries for the security evaluation of machine learning algorithms.

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).

TEACHERS AND EXAM BOARD

Exam Board

FABIO ROLI (President)

LUCA DEMETRIO

LUCA ONETO (President Substitute)

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).

Exam schedule