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
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
Cyber-physical systems security
Credits: 3
Language: English
Teacher: Massimo Merro
Elements of Machine Teaching: Theory and Appl.
Credits: 3
Language: English
Teacher: Ferdinando Cicalese
Fondamenti di Linguaggi Quantistici
Credits: 3
Language: English
Teacher: Margherita Zorzi
Introduction to Blockchain
Credits: 3
Language: English
Teacher: Sara Migliorini
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
Multimodal Learning and Applications
Credits: 5
Language: English
Teacher: Cigdem Beyan
Elements of Machine Teaching: Theory and Appl. (2023/2024)
Teacher
Referent
Credits
3
Language
English
Class attendance
Free Choice
Location
VERONA
Learning objectives
Machine Teaching studies how efficiently a teacher can guide a learner to acquire a target hypothesis.
The classic works date back to the 1990’s [Shinohara91,Goldman95] consider the setting where the teacher sends in one shot a set of labeled examples to the learner, who then has to output the correct target hypothesis. In the more recent studies, the focus has been on the interactive setting, where the Teacher and Leaner interact over multiple rounds. In each round, the teacher sends examples to the learner, who returns some feedback; this process continues until the learner reaches the target hypothesis (or a good approximation of it). Machine teaching models have proved useful in several contexts, e.g., crowd sourcing, intelligent tutoring systems, analysis of training set attacks. Moreover, commercial tools are under development by the Microsoft Machine Teaching Group, as detailed on their web page, which are based on, or employ, the paradigm of machine teaching, e.g., PICL, which leverages the selection of examples that maximize the training value of the interaction with the teacher; LUIS for natural language understanding; and other projects on building models for autonomous systems, and tools enabling non-experts of machine learning to build their models.
Prerequisites and basic notions
Basic knowledge of algorithm analysis and discrete probability
Program
Foundations: From PAC learning to Active learning, to Machine Teaching; Teaching dimension concepts (batch, sequential, recursive, VC-dimension and sample compression); Interactive Machine Teaching and Black Box machine teaching; Application: human/robot/computer interaction, training-set attacks, crowdsourcing.
When and where
Lectures (blackboard and slides)
Learning assessment procedures
Reading assignments and oral discussion
Assessment
Understanding of the basic concepts and ability to apply them in new contexts
Criteria for the composition of the final grade
The result will be 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
INFORMATION: ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Arts and Humanities]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Law and Economics]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Life and Health Sciences - 1 st Session]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Life and Health Sciences - 2 nd Session]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Natural Sci. and Engineering-1st Session]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Natural Sci. and Engineering-2nd Session]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC WRITING SKILLS [Arts and Humanities]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC WRITING SKILLS [Law and Economics]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC WRITING SKILLS [Life and Health Sciences - 1 st Session]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC WRITING SKILLS [Life and Health Sciences - 2 nd Session]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC WRITING SKILLS [Natural Sci. and Engineering-1st Session]
Credits: 2,5
Language: English
INFORMATION: ENGLISH FOR ACADEMIC WRITING SKILLS [Natural Sci. and Engineering-2nd 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
BASIC LEVEL STATISTICS
Credits: 2,5
Language: English
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: 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: English
Teacher: Giuseppe Verlato
Analisi di sopravvivenza: test log-rank, curve di sopravvivenza di Kaplan-Meier, modello di regressione di Cox
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
IMPARA IL MARKETING DIGITALE
Credits: 1,5
Language: English
Italian Poetry abroad
Credits: 1
Language: Italiano
Teacher: Massimo Natale
COSTRUISCI IL TUO BUSINESS MODEL CANVAS
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
LA COMUNICAZIONE UMANISTICA: OPPORTUNITA' E RISCHI
Credits: 1
Language: Italiano
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 |
