(MLA) Key Topics
The Machine Learning professionals we train are keen to apply state of the art algorithms to industry and social challenges. To provide them with the necessary tools, students will be given both the scientific foundations to deeply understand the main ML concepts, and the technical preparation to implement new algorithms in the real world.
| Machine Learning I | Merone |
| Machine Learning II | Merone |
| Deep Leaarning | Capirchio |
| Big data analysis with Pandas | Cecconi/Mirino |
| Fundamentals of reinforcement learning | Santucci |
| Open-ended learning | Baldassarre/Cartoni |
| Sentiment analysis | De Persiis/Reforgiato |
| Commercial and serious game | Schembri |
| Data augmentation | D’Amore |
| AI Lab: ML Vs Deep network and Perception of motion | Tamantini |
| Natural Language Processing | Reforgiato |
| AI Lab: Deep network for NLP | De Persis |
| Knowledge graph and semantic web |
Nuzzolese |
| Data pre-processing | Mirino |