Biometric recognition of individuals based on the observation of behavioural and biological characteristics, such as face, iris, or fingers is widespread in access control applications. Such methods can be used for verification or identification applications and constitute a comfortable alternative to knowledge based or token based methods.

In the last decade, the availability of larger amounts of data and higher computational resources has led to a revolution in machine learning, leading to the so called deep learning architectures. These algorithms can provide higher classification accuracy or generate very realistic synthetic images or audios.

Current advances in AI and machine learning allow us to automate certain tasks or enhance the obtained results. However, processing personal data without appropriate safety measures can also lead to undesirable disclosure of sensitive information. Indeed, machine learning models often contain precise information about individual data points that were used to train them. We thus have to protect the privacy of the individuals, trying at the same time to maximise the utility of the machine learning model.

Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task. It allows computers to learn and make decisions without being explicitly programmed, by identifying patterns in data and improving its ability to perform specific tasks. In this course we will understand how ML can be applied to solve real-world problems and gain a solid foundation in classical ML.