Training and Research
PhD Programme Courses/classes
Non monotonic reasoning
Credits: 3
Language: English
Teacher: Matteo Cristani
Sustainable Embodied Mechanical Intelligence
Credits: 3
Language: English
Teacher: Giovanni Gerardo Muscolo
Brain Computer Interfaces
Credits: 3
Language: English
Teacher: Silvia Francesca Storti
A practical interdisciplinary PhD course on exploratory data analysis
Credits: 4
Language: English
Teacher: Prof. Vincenzo Bonnici (Università di Parma)
Multimodal Learning and Applications
Credits: 5
Language: English
Teacher: Cigdem Beyan
Introduction to Blockchain
Credits: 3
Language: English
Teacher: Sara Migliorini
Autonomous Agents and Multi-Agent Systems
Credits: 5
Language: English
Teacher: Alessandro Farinelli
Cyber-physical systems security
Credits: 3
Language: English/Italian
Teacher: Massimo Merro
Foundations of quantum languages
Credits: 3
Language: English
Teacher: Margherita Zorzi
Advanced Data Structures for Textual Data
Credits: 3
Language: English
Teacher: Zsuzsanna Liptak
AI and explainable models
Credits: 5
Language: English
Teacher: Gloria Menegaz, Lorenza Brusini
Automated Software Testing
Credits: 4
Language: English
Teacher: Mariano Ceccato
Elements of Machine Teaching: Theory and Appl.
Credits: 3
Language: English
Teacher: Ferdinando Cicalese
Introduction to Quantum Machine Learning
Credits: 4
Language: English
Teacher: Alessandra Di Pierro
Laboratory of quantum information in classical wave-optics analogy
Credits: 3
Language: English
Teacher: Claudia Daffara
AI and explainable models (2023/2024)
Academic staff
Referent
Credits
5
Language
English
Class attendance
Free Choice
Location
VERONA
Learning objectives
Artificial Intelligence has become a fundamental instrument in fields like biomedicine and neurosciences, from the discovery of new numerical biomarkers to support to the diagnosis. However, especially in the previously mentioned fields, the machine and deep learning methods employed for the analysis are often seen as black box due to the million of mathematical operations they perform. Explainable models have been developed with the precise scope of shedding light on the mechanisms leading to the results. Based on this premise, this course aims at providing the students knowledge about the main machine learning methods and explainable models at the state of the art that are mostly exploited in the field, providing both theoretical bases and implementation tools.
Prerequisites and basic notions
The laboratory sessions will be in Python as this is by far the most exploited tool for Artificial Intelligence applications in any field. To promote the participation of students from other fields beyond computer science (e.g., biomedical field), the first lesson can be devoted to the introduction of the main concepts so to provide also those with no background the tools needed for attending the hands-on sessions.
Program
- Fundamentals of AI: machine learning step-by-step
- Opening the black box: the eXplainable Artificial Intelligence
- The main models in the light of explainability
- A hint into agnostic post-hoc explainability models
Bibliography
Didactic methods
The participation to the course will be fully in person. Each lecture will start by setting the theoretical bases of the following hands-on session. In particular, the hands-on sessions will include both the illustration and discussion of pieces of code and the exercises for students aiming at solving the considered problem.
- Friday, 14th of June 2024. From 9:00 to 13:00 in Aula T.06 (Cà Vignal 3)
- Thursday, 20th of June 2024. From 9:00 to 13:00 in Aula T.05 (Cà Vignal 3)
- Friday, 21st of June 2024. From 9:00 to 13:00 in Aula T.05 (Cà Vignal 3)
- Tuesday, 25th of June 2024. From 9:00 to 13:00 in Aula B (Cà Vignal 1)
- Thursday, 27th of June 2024. From 9:00 to 13:00 in Aula T.05 (Cà Vignal 3)
Learning assessment procedures
Development of a brief project consisting of the application of what learned during the lessons.
Assessment
Correct application of what learned during the course
Criteria for the composition of the final grade
Pass/Fail
