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
The educational activities of type D are chosen by the student, those of type F are further knowledge useful for entering the world of work (internships, soft skills, project works, etc.). According to the Didactic Regulations of the Course, some activities can be chosen and included autonomously in the booklet, others must be approved by a special committee to verify their consistency with the study plan. Type D or F educational activities can be covered by the following activities.
1. Teachings taught at the University of Verona.
Include the teachings 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 include it autonomously during the period in which the study plan is open; otherwise, the student must submit 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, the following are recognized:
- English language: 3 CFUs are recognized for each level of proficiency above the one required by the course of study (if not already recognized in the previous course of study).
- Other languages and Italian for foreigners: 3 cfu are recognized for each proficiency level starting from A2 (if not already recognized in the previous study cycle).
These cfu will be recognized, up to a maximum of 6 cfu in total, as type F if the teaching plan allows, or as type D. Additional elective credits for language knowledge may be recognized only if consistent with the student's educational project and if adequately justified.
Booklet entry mode: request the certificate or equivalency to the CLA and send it to the Student Secretariat - Careers for career entry of the exam, via email: carriere.scienze@ateneo.univr.it
3. Soft 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
Booklet entry mode: the teaching is not expected to be included in the curriculum. Only after obtaining the Open Badge, the CFUs in the booklet will 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. Stage/internship period
In addition to the CFUs required by the curriculum (check carefully what is indicated on the Didactic Regulations) here 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.
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Introduction to smart contract programming for ethereum | D |
Sara Migliorini
(Coordinator)
|
| 1° 2° | BEYOND ARDUINO: FROM PROTOTYPE TO PRODUCT WITH STM MICROCONTROLLER | D |
Franco Fummi
(Coordinator)
|
| 1° 2° | APP REACT PLANNING | D |
Graziano Pravadelli
(Coordinator)
|
| 1° 2° | HW components design on FPGA | D |
Franco Fummi
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | LaTeX Language | D |
Enrico Gregorio
(Coordinator)
|
| 1° 2° | Rapid prototyping on Arduino | D |
Franco Fummi
(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)
|
Data management and machine intelligence (2024/2025)
Teaching code
4S012352
Credits
12
Coordinator
Language
English
Also offered in courses:
- Deep Learning of the course Master's degree in Computer Engineering for Robotics and Smart Industry
- Advanced database & information systems of the course Master's degree in Computer Engineering for Robotics and Smart Industry
Courses Single
Authorized
The teaching is organized as follows:
Learning objectives
The course is composed by two modules. The objective of the first, data management systems, is to allow students to acquire in-depth knowledge of the methodologies and tools necessary to manage large amounts of data in new systems not based on the relational model (we will therefore consider systems based on semi-structured or document-based models, NoSQL and extended models with time and space dimensions). In particular, the systems that must memorize data also produced by sensors and mobile devices will be considered, so that a correct integration of these new data sources with the corporate information system is possible. At the end of the course, the student will be able to design and query non-traditional databases with typical tools of the NoSQL approach. The objective of the second, "deep learning",is to provide the fundamentals of neural networks as evolution of linear models, including architecture, activation functions and backpropagation. He will know the basics of the optimization algorithms used in the training of neural networks. Furthermore, it will provide knowledge on basic neural network architectures: the student will know various types of neural networks such as convolutional neural networks (CNN) for image analysis, recurrent neural networks (RNN) for sequential data and transformers for natural language processing. He/she will be able to understand the design principles behind these architectures and their applications, and the underlying mathematical theories. He/she will also be able to apply the techniques to real-world problems, understanding what data inputs and outputs should be. The course will present also methods for data preprocessing and augmentation. In the second part the course will also present advanced neural network architectures, used to tackle natural language processing (NLP) and computer vision (CV) problems such as text classification, object recognition and machine translation. In this regard, the student will know how to use advanced tools such as variational encoders, generative adversarial networks, NERF, and large language models. Interpretability: The student will learn the basics of interpretability of a neural network. This will make it possible to offer guarantee tools on the results produced by neural networks. Deep Learning Frameworks and Tools: The student will have gained hands-on experience with popular deep learning frameworks such as TensorFlow, PyTorch, and Keras. You will be able to build, train, and evaluate deep learning models using these frameworks.
Prerequisites and basic notions
Data Management Module: Fundamentals of computer science (programming logic, basic data structures), operating systems (file management, process and thread concepts), basic mathematics (algebra, logic). TCP/IP and network models.
Deep Learning Module: Linear Algebra (Vectors and matrices, matrix operations, eigenvalues and eigenvectors, vector spaces and their transformations), Calculus (derivatives and integrals, partial derivatives), Probability and statistics, Programming (Python or Matlab), Machine learning (supervised and unsupervised models)
Bibliography
Criteria for the composition of the final grade
The final grade is assigned as the sum of the grades of the two modules, which will range from 9 (pass) to 15 (the maximum for a course). Honors will be assigned by mutual agreement between the teachers of the two modules.
