Training and Research
PhD Programme Courses/classes - 2023/2024
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
Cyber-physical systems security
Credits: 3
Language: English/Italian
Teacher: Massimo Merro
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
Autonomous Agents and Multi-Agent Systems
Credits: 5
Language: English
Teacher: Alessandro Farinelli
Elements of Machine Teaching: Theory and Appl.
Credits: 3
Language: English
Teacher: Ferdinando Cicalese
Foundations of quantum languages
Credits: 3
Language: English
Teacher: Margherita Zorzi
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
PhD school courses/classes - 2023/2024
Please note: Additional information will be added during the year. Currently missing information is labelled as “TBD” (i.e. To Be Determined).
PhD students must obtain a specified number of CFUs each year by attending teaching activities offered by the PhD School.
First and second year students must obtain 8 CFUs. Teaching activities ex DM 226/2021 provide 5 CFUs; free choice activities provide 3 CFUs.
Third year students must obtain 4 CFUs. Teaching activities ex DM 226/2021 provide 2 CFUs; free choice activities provide 2 CFUs.
Registering for the courses is not required unless explicitly indicated; please consult the course information to verify whether registration is required or not. When registration is actually required, no confirmation e-mail will be sent after signing up.
Teaching Activities ex DM 226/2021: Linguistic Activities
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Arts and Humanities]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Law and Economics]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Life and Health Sciences - 1 st Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Life and Health Sciences - 2 nd Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Natural Sci. and Engineering-2nd Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Natural Sci. and Engineering-1st Session]
Credits: 2,5
Language: English
Teacher: Monica Antonello
ENGLISH FOR ACADEMIC WRITING SKILLS [Arts and Humanities]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Law and Economics]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Life and Health Sciences - 1 st Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Natural Sci. and Engineering-1st Session]
Credits: 2,5
Language: English
Teacher: Monica Antonello
ENGLISH FOR ACADEMIC WRITING SKILLS [Natural Sci. and Engineering-2nd Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Life and Health Sciences - 2 nd Session]
Credits: 2,5
Language: English
Teaching Activities ex DM 226/2021: Research management and Enhancement
SEMINARIO AVANZATO SULLE RISORSE BIBLIOTECARIE PER LA RICERCA [Arts and Humanities]
Credits: 2,5
Language: Italian
Teacher: Donatella Boni
SEMINARIO AVANZATO SULLE RISORSE BIBLIOTECARIE PER LA RICERCA [Law and Economics]
Credits: 2,5
Language: Italian
Teacher: Luisella Zocca
SEMINARIO AVANZATO SULLE RISORSE BIBLIOTECARIE PER LA RICERCA [Scientific Area]
Credits: 2,5
Language: Italian
Teacher: Elena Scanferla
Teaching Activities ex DM 226/2021: Statistics and Computer Sciences
INTRODUCTION TO PROBABILITY (MODULE I)
Credits: 1
Language: English
Teacher: Marco Minozzo
INTRODUCTION TO PROBABILITY (MODULE II)
Credits: 1
Language: English
Teacher: Marco Minozzo
INTRODUCTION TO STATISTICAL INFERENCE
Credits: 1
Language: English
Validità e affidabilità delle misure e dei test diagnostici
Credits: 0,5
Language: English
Teacher: Alessandro Marcon
BASIC LEVEL STATISTICS
Credits: 2,5
Language: English
BASIC LEVEL STATISTICS
Credits: 2,5
Language: Italian
Statistical analysis with R - module I
Credits: 1
Language: Italian
Teacher: Erica Secchettin
GENERALIZED LINEAR MODELS: LOGISTIC REGRESSION, LOGLINEAR MODEL, POISSON MODEL
Credits: 2
Language: English
Teacher: Lucia Cazzoletti
Disegno dello studio nella ricerca osservazionale e sperimentale
Credits: 1,5
Language: English
Teacher: Alessandro Marcon
Calcolo della numerosità campionaria in funzione di una precisione o potenza statistica prefissata
Credits: 1
Language: English
Teacher: Giuseppe Verlato
Introduzione alla meta-analisi per la ricerca biomedica (revisione della letteratura, raccolta dei dati, costruzione del database)
Credits: 1
Language: English
Teacher: Giuseppe Verlato
Applicazioni della meta-analisi in campo epidemiologico e medico
Credits: 1
Language: Inglese
Teacher: Giuseppe Verlato
Survival analysis: log-rank test, Kaplan-Meier survival curves, Cox regression model
Credits: 1,5
Language: Inglese - English
Teacher: Simone Accordini
INTERMEDIATE STATISTICS [Recommended for Human Sciences]
Credits: 2,5
Language: English
INTERMEDIATE STATISTICS [Tutti i corsi di studio]
Credits: 2,5
Language: English
Statistical analysis with R - module II
Credits: 2
Language: Italian
Teacher: Erica Secchettin
Teaching Activities: Free choice
PROTECTING PSYCHOLOGICAL WELL-BEING IN THE PHD PROGRAM: WHAT DO WE NEED TO CONSIDER FOR BEING A GOOD SCIENTIST: BEST PRACTICE AND THE ETHICS OF SCIENCE
Credits: 1
Language: inglese
Teacher: Paola Cesari
QUANDO LA RICERCA SI FA ETICA (PERCORSO ORGANIZZATO E FINANZIATO DAL TEACHING AND LEARNING CENTER DI UNIVR)
Credits: 2
Language: Italian
Teacher: Roberta Silva
LA COMUNICAZIONE UMANISTICA: OPPORTUNITA' E RISCHI
Credits: 1
Language: Italiano
Italian Poetry abroad
Credits: 1
Language: Italiano
Teacher: Massimo Natale
BUSINESS MODEL CANVAS PILL
Credits: 1,5
Language: English
IMPARA IL MARKETING DIGITALE
Credits: 1,5
Language: English
APPROCCI E METODOLOGIE PARTECIPATIVE NELLA RICERCA CON GLI ATTORI DEL TERRITORIO
Credits: 1,5
Language: Italian
Teacher: Cristiana Zara
DOING INTERVIEWS IN QUALITATIVE RESEARCH
Credits: 1,5
Language: English
Teacher: Chiara Sità
DIFFERENTIAL DIAGNOSIS OF DEMYELINATING DISEASES OF THE CENTRAL NERVOUS SYSTEM
Credits: 2
Language: English
Teacher: Alberto Gajofatto
IL SONNO E I SUOI DISTURBI: FOCUS SULLE PARASONNIE E I DISTURBI DEL MOVIMENTO IN SONNO
Credits: 1
Language: English
Teacher: Elena Antelmi
IMAGING TECHNIQUES FOR BODY COMPOSITION ANALYSIS
Credits: 1
Language: English
Teacher: Carlo Zancanaro
OPEN SCIENCE: THE MIGHTY STICK AGAINST "BAD" SCIENCE
Credits: 2
Language: English
Teacher: Alberto Scandola
THE EMPIRICAL PHENOMENOLOGICAL METHOD (EPM): THEORETICAL FOUNDATION AND EMPIRICAL APPLICATION IN EDUCATIONAL AND HEALTHCARE FIELDS
Credits: 2
Language: English
THE PATHWAY OF OXYGEN: CAUSE OF HYPOXEMIA
Credits: 1
Language: English
Teacher: Carlo Capelli
Faculty
+39 045 802 7980
cigdem.beyan@univr.it
mariano.ceccato@univr.it
mila.dallapreda@univr.it
giovannigerardo.muscolo@univr.it
PhD students
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Guidelines for PhD students
Below you will find the files that contain the Guidelines for PhD students and rules for the acquisition of ECTS credits (in Italian: "CFU") for the Academic Year 2023/2024.
Documents
| Title | Info File |
|---|---|
Dottorandi: linee guida generali (2023/2024)
|
pdf, it, 93 KB, 26/02/24 |
|
PhD students: general guidelines (2023/2024)
|
pdf, en, 94 KB, 26/02/24 |
