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.

Type D and Type F activities

This information is intended exclusively for students already enrolled in this course.
If you are a new student interested in enrolling, you can find information about the course of study on the course page:

Laurea magistrale in Artificial Intelligence - Enrollment from 2025/2026

Type D learning activities are the student's choice, type F activities are additional knowledge useful for job placement (internships, transversal skills, project works, etc.). According to the Teaching Regulations of the Course, some activities can be chosen and entered independently in the booklet, others must be approved by a special committee to verify their consistency with the study plan. Type D or F learning activities can be covered by the following activities.

1. Modules taught at the University of Verona

Include the modules listed below and/or in the Course Catalogue (which can also be filtered by language of delivery via Advanced Search).

Booklet entry mode: if the teaching is included among those listed below, the student can enter it independently during the period in which the curriculum is open; otherwise, the student must make a request to the Secretariat, sending the form to carriere.scienze@ateneo.univr.it during the period indicated.

2. CLA certificate or language equivalency

In addition to those required by the curriculum/study plan, the following are recognized for those matriculated from A.Y. 2021/2022:

  • English language: 3 CFUs are recognized for each level of proficiency above that required by the course of study (if not already recognized in the previous course of study).
  • Other languages and Italian for foreigners: 3 CFUs are recognized for each proficiency level starting from A2 (if not already recognized in the previous study cycle).

These CFUs will be recognized, up to a maximum of 6 CFUs in total, of type F if the study plan allows it, or of type D. Additional elective credits for language knowledge may be recognized only if consistent with the student's educational project and if adequately justified.

Those enrolled until A.Y. 2020/2021 should consult the information found here.

Method of inclusion in the bookletrequest the certificate or equivalency from CLA and send it to the Student Secretariat - Careers for the inclusion of the exam in the career, by email: carriere.scienze@ateneo.univr.it

3. Transversal skills

Discover the training paths promoted by the University's TALC - Teaching and learning center intended for students regularly enrolled in the academic year of course delivery https://talc.univr.it/it/competenze-trasversali

Mode of inclusion in the booklet: the teaching is not expected to be included in the curriculum. Only upon obtaining the Open Badge will the booklet CFUs be automatically validated. The registration of CFUs in career is not instantaneous, but there will be some technical time to wait.  

4. Contamination lab

The Contamination Lab Verona (CLab Verona) is an experiential course with modules on innovation and enterprise culture that offers the opportunity to work in teams with students from all areas to solve challenges set by companies and organisations.  

Upon completion of a CLab, students will be entitled to receive 6 CFU (D- or F-type credits).  

Find out more:  https://www.univr.it/clabverona 

PLEASE NOTE: In order to be admitted to any teaching activities, including those of your choice, you must be enrolled in the academic year in which the activities in question are offered. Students who are about to graduate in the December and April sessions are therefore advised NOT to undertake extracurricular activities in the new academic year in which they are not enrolled, as these graduation sessions are valid for students enrolled in the previous academic year. Therefore, students who undertake an activity in an academic year in which they are not enrolled will not be granted CFU credits.  

5. Internship/internship period

In addition to the CFUs stipulated in the curriculum/study plan (check carefully what is indicated on the Teaching Regulationshere you can find information on how to activate the internship. 

Check in the regulations which activities can be Type D and which can be Type F.

Please also note that for traineeships activated after 1 October 2024, it will be possible to recognise excess hours in terms of type D credits limited only to traineeship experiences carried out at host organisations outside the University.

Academic year:

Teaching code

4S010683

Coordinator

Daniele Meli

Credits

6

Language

English en

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Period

Semester 1  dal Oct 1, 2024 al Jan 31, 2025.

Courses Single

Authorized

Learning objectives

This course builds on knowledge about statistical/machine/deep learning methods and aims at providing means to understand the “why” and the “how” of their outcomes. After introducing the basic concepts, a taxonomy of the existing methods will be provided, then the main state-of-the-art approaches for neurosymbolic AI will be illustrated. The theoretical part will be complemented by practical sessions where the concepts that have been acquired will be put in practice considering specific case-studies.

At the end of the course the students will have acquired fundamental skills about explainability, interpretability, randomness and causality; the knowledge of the main methods for interpretability (intrinsic methods, post-hoc, model-specific, model-agnostic, local, global, etc.), of the related properties (sensibility, implementation invariance, separability, stability, completeness, correctness, compactness), of the main types of explanations and their properties (accuracy, fidelity, consistency, stability, comprehensibility, certainty and relevance), and of the main visualization methods (activation maps, LRP, GradCam). Additionally, students will need to demonstrate knowledge of state-of-the-art approaches to neuro-symbolic artificial intelligence, with main focus on: standard deep learning; symbolic solvers that use neural networks as sub-routines for state estimation; hybrid systems with neural network and symbolic system specialized on complementary tasks with interaction through input/output; symbolic knowledge compiled in the training set of a neural network; neural computing systems that contain symbolic reasoning systems (type 1 and 2 reasoning).

Examination methods

To pass the exam, students must demonstrate:
- to have understood the theoretical and methodological aspects of the teaching
- to know how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions and projects.

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; principles of explainable human-machine interaction (human-machine collaboration, extraction of explainable patterns from the interaction with the machine).

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

inglese / english