The course is intended to get the students acquained with the latest trends in artificial intelligence. It will provide the students with an array of the most recent models and applications ranging from deep neural networks to Generative AI.

By the end of the course, students will be able to understand and implement advanced machine learning and artificial intelligence models. By means of company-driven group projects, they will also understand their interplay in a business-oriented scenario.

The course will familiarise the students with the latest following applications in the fields of (generative) artificial intelligence: a) feature selection and dimensionality reduction; b) optimization techniques; c) time series analysis; d) generative artificial intelligence; e) natural language processing; f) graph-based machine learning

All the class material will be available on the e-learning platform (slides, lecture notes, and reference papers).

Lectures and lab sessions. Students' participation during lectures is strongly encouraged.

Competences will be assessed via a group project and a theoretical assessment. There will be two (optional) intermediate tests, the sum of which will account for the theoretical assessment. Students that will not take (or pass) both intermediate tests during the course are required to take an oral examination at the end of the course.

A thesis may be assigned, upon specific request to the instructor, to students who have an average grade above 27/30 and demonstrate a serious and motivated interest in the course topics.