Hours:
20 hours (5 credits)
Room:
Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa - Ground Floor
To register to the course, click here
Short Abstract:
Machine Learning has assumed a dominant role in the design of intelligent systems and their various application domains. The objective of this course is to present current trends in the development of Machine Learning, identify challenges and discuss ways of addressing them.
Course Contents in brief:
Introductory comments
The key agenda of Machine Learning. Main concepts. Deployment of Machine Learning and fundamental quests. Challenges of Machine Learning: credibility (confidence), interpretability and explainability, privacy.
Granular Computing: a primer
Concepts, motivation, examples. Design of information granules, rule-based architectures: symbolic- subsymbolic perspective. Learning schemes.
Credibility of ML architectures and their results
Motivation. Granular embedding and Gaussian Process augmentation. Mechanisms of active learning.
Interpretability and explainability
Processes of interpretability and explainability. Inductive and deductive reasoning. Counterfactual reasoning. Local linear models. Shapley value.
Privacy in ML: a case of federated learning
Motivating factors behind federated learning: coping with data islands, average and gradient federated learning, Federated learning-based rule design, granular assessment and performance analysis.
Schedule:
- 19/4/2024, 13.30-18.30
- 22/4/2024, 8.30-13.30
- 23/4/2024, 8.30-13.30
- 24/4/2024, 8.30-13.30
