Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

Study Plan

A.A. 2026/2027 A.A. 2025/2026

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.

1° Year

ModulesCreditsTAFSSD
6
B
INF/01
12
B
ING-INF/05
12
B
INF/01

2° Year  It will be activated in the A.Y. 2026/2027

ModulesCreditsTAFSSD
6
B
INF/01
Final exam
24
E
-
It will be activated in the A.Y. 2026/2027
ModulesCreditsTAFSSD
6
B
INF/01
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
2 modules among:
- 1st year - Knowledge representation, Natural Language Processing, HCI - Multimodal Systems - delivered in 2025/2026
- 2nd year - AI & cloud - delivered in 2026/2027
- 1st and 2nd year - Advanced programming for AI, Computer vision & deep learning - delivered in 2025/2026 and in 2026/2027
6
B
ING-INF/05
6
B
INF/01
6
B
INF/01
6
B
ING-INF/05
6
B
INF/01
6
B
ING-INF/05
Between the years: 1°- 2°
2 courses among (mutually exclusive with the previous ones):
- 1st year - Knowledge representation, Natural language processing, HCI - multimodal systems - delivered in 2025/2026
- 2nd year - AI & cloud, Visual intelligence - delivered in 2026/2027
- 1st and 2nd year - Advanced programming for AI, Computer Vision & deep learning, Statistical learning - delivered in 2025/2026 and in 2026/2027
6
C
ING-INF/05
6
C
INF/01
6
C
INF/01
6
C
ING-INF/05
6
C
INF/01
6
C
ING-INF/05
6
C
INF/01
6
C
ING-INF/06
Between the years: 1°- 2°
2 courses among the following
- A.A. 2025/2026 Network Science not activated
- A.A. 2026/2027: Complex Systems not activated
6
C
ING-INF/05
6
C
INF/01
6
C
FIS/02
6
C
INF/01
6
C
MAT/08
6
C
MAT/09
6
C
INF/01 ,ING-INF/05
6
C
INF/01
6
C
FIS/02
6
C
INF/01
6
C
MAT/06
6
C
INF/01
Between the years: 1°- 2°
1 course among the following
6
C
SECS-S/06
6
C
IUS/07
6
C
IUS/02
6
C
M-FIL/02
6
C
IUS/08
6
C
IUS/17
6
C
IUS/04
6
C
IUS/01
Between the years: 1°- 2°
Further activities: 3 CFU training and 3 CFU further language skill or 6 CFU training. International students (i.e. students who do not have an Italian bachelor’s degree) must compulsorily gain 3 CFU of Italian language skills (at least A2 level) and 3 CFU training.
6
F
-
Between the years: 1°- 2°
12
D
-

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Explainable AI  (2026/2027)

Teaching code

4S010683

Teacher

Daniele Meli

Coordinator

Daniele Meli

Credits

6

Language

English en

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Period

I semestre dal Oct 1, 2026 al Jan 29, 2027.

Courses Single

Authorized

Lessons timetable Seminars 0

Learning objectives

This course delves into artificial intelligence (AI) methods, with the aim of providing the means to understand the "why" and "how" of their results. After introducing the basic concepts of explainable AI, including a taxonomy of existing methods, the main state-of-the-art approaches and algorithms will be explained in detail. Particular emphasis will be given to AI techniques that can be explained or interpreted by construction, i.e., that ensure easy readability by humans, increasing trust in AI. In this context, the focus will be mainly on causal analysis and neurosymbolic approaches. The theoretical part will be complemented by practical sessions in which concepts and algorithms will be implemented and/or applied in specific case studies.

Prerequisites and basic notions

Model-free and model-based RL; basics of statistics; basics of automated reasoning and propositional / first-order / temporal logics.

Program

explainability and interpretability for AI; concepts and algorithms for the causal analysis of data in the form of time series (Granger causality, main assumptions of causality, main algorithms including PCMCI), with application to the identification of patterns and the identification of anomalies; autonomous planning based on formal methods (logic programming, answer set programming); learning of logical explanations from data (inductive logic programming, induction in the semantics of answer sets), with application to the interpretation of policies for reinforcement learning agents; neurosymbolic planning and learning, combining reinforcement learning techniques with techniques based on logic programming and logic induction; post-hoc explainability with counterfactual explanations and applications to reinforcement learning

Didactic methods

Almost all theoretical lectures will be linked to lab sessions with the computer for practical implementation of concepts and algorithms, with the support of a teaching assistant

Learning assessment procedures

The exam will consist of 2 parts:
1. theoretical interview XOR practical project or study of a research paper (in agreement with the teacher)
2. presentation of lab assignments

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Evaluation criteria

theoretical and implementative skills acquired regarding the topics of lab and theoretical lessons.

Criteria for the composition of the final grade

60% theory / project / paper; 40% lab assignments

Exam language

English