Disciplinary courses
Starting with the cycle 41, the disciplinary courses are distinguished between those designed with a broader audience in mind, that are given each year in the November-March period, and the more specialized courses. PhD students are invited to take at least 3 of the broader exams during their first year. The first-year courses are:
- Optimization Methods
- Efficient Algorithms
- Foundational Models
- Machine Learning Paradigms
- Design of Experiments
- Performance Evaluation and Data Centric AI
The more specialized courses are instead given once every two years. This allows each PhD student to take any of those courses during its PhD. The spcialized courses are:
- Adversarial Attacks on Classification and Alignment in Foundational Models
Reinforcement Learning - Causal Networks: Learning and Inference
- Machine Learning for the Edge
- Natural Language Processing
- Neuro-Symbolic Integration
- Geometry Processing and Machine Learning for Geometric Data
The complete list of disciplinary courses given is available to all PhD students
Interdisciplinary courses
The interdisciplnary courses cover an even broader set of topics, including scientific aspects, technical aspects, soft skills, ethics, and sustainability.