Teacher:
Content: The course aims at compactly preseningt the main paradigms of machine learning (supervised, unsupervised and reinforcement learning) while
also presenting their statistical basis (statistical learning theory). The most recent developments in terms of explainability and interpretability of ML models will also be presented.
Topics:
- Statistical Learning theory
- Supervised Learning VS Unsupervised Learning
- Classification, Regression and Clustering
- Reinforcement Learning (Pitch)
- Explainable/interpretable ML
- Practical Problems and Big Failure