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

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.

Semester 1  From 10/1/24 To 1/31/25
years Modules TAF Teacher
1° 2° Introduction to quantum mechanics for quantum computing D Claudia Daffara (Coordinator)
1° 2° Python programming language [English edition] D Carlo Combi (Coordinator)
1° 2° APP REACT PLANNING D Graziano Pravadelli (Coordinator)
Semester 2 From 3/3/25 To 6/13/25
years Modules TAF Teacher
1° 2° Python programming language [Edizione in italiano] D Carlo Combi (Coordinator)
1° 2° Programming Challanges D Romeo Rizzi (Coordinator)
1° 2° Protection of intangible assets (SW and invention)between industrial law and copyright D Mila Dalla Preda (Coordinator)
List of courses with unassigned period
years Modules TAF Teacher
1° 2° Mathematics mini courses Giacomo Albi (Coordinator)

Teaching code

4S008278

Coordinator

Giacomo Albi

Credits

6

Also offered in courses:

Language

English en

Scientific Disciplinary Sector (SSD)

MAT/08 - NUMERICAL ANALYSIS

Period

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

Courses Single

Authorized

Learning objectives

After successful completion of the module students will be able to understand and apply the basic notions, concepts, and methods of computational linear algebra, convex optimization and differential geometry used for data analysis. In particular, they will master the use of singular value decomposition method as well as random matrices for low dimensional data representations, including fundamentals of sparse recovery problems, as e.g., compressed sensing, low rank matrix recovery, and dictionary learning algorithms. The students will be also able to manage the representation of data as clusters around manifolds in high dimensions and in random graphs, acquiring methods to construct local charts and clusters for the data. In complementary laboratory sessions they will get acquainted with suitable programming tools and environment in order to analyse relevant case studies.

To pass the exam, students must demonstrate:
- to have understood the principles underlying computational linear algebra, convex optimization and differential geometry applied to data analysis.
- to be able to present arguments on the topics of the course in a precise and organic way without digressions
- 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

Differential calculus, linear algebra, basics of numerical analysis, basic knowledge of a programming language

Program

* Introduction to optimization
- optimality conditions
- numerical methods
* Singular Value Decomposition:
- Best k-rank approximation, Randomized SVD
- Principal Component Analysis, Pseudo-Inverse.
* Compressed Sensing
- Basis pursuit problem: l1-minimization and sparse recovery
- Application to signals and images reconstruction.
* Data Analysis
- Dimensionality reduction techniques: (Local Linear Embedding, ISOMAP, diffusion map).
- Supervised learning for classification: Support Vector Machine
- Unsupervised learning for clustering: K-means.
- Artificial Neural Networks and applications.

Bibliography

Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Didactic methods

* Lectures and homeworks
* Code development and examples
* Individual or group projects
The rights of students will be preserved in situations of travel limitation or confinement due to national provisions to combat COVID or in particular situations of fragile health. In these cases, you are invited to contact the teacher directly to organize the most appropriate remedial strategies.

Learning assessment procedures

To pass the exam, students must demonstrate:
- to have understood the principles underlying computational linear algebra, convex optimization and differential geometry applied to data analysis.
- to be able to present arguments on the topics of the course in a precise and organic way without digressions
- to know how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions and projects.
The exam consists of an oral examination with written questions and discussion. The development of a project is encouraged (but not mandatory) as an integration of the oral examination.

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

The student must be able to formalize and solve data analysis problems by using, adapting and developing advanced numerical methods in the context of optimization, numerical linear algebra seen during teaching.

Criteria for the composition of the final grade

Oral exam with questions (85%), resolution of exercises and home project (15%)

Exam language

Inglese