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 Medical bioinformatics - Enrollment from 2025/2026Type 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 booklet: request 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 Regulations): here information on how to activate the internship.
Check in the regulations which activities can be Type D and which can be Type F.
Modules and other activities that can be entered independently in the booklet
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 2° | Introduction to Robotics for students of scientific courses. | D |
Paolo Fiorini
(Coordinator)
|
| 2° | Matlab-Simulink programming | D |
Bogdan Mihai Maris
(Coordinator)
|
| 2° | Rapid prototyping on Arduino | D |
Franco Fummi
(Coordinator)
|
| 2° | Programming Challanges | D |
Romeo Rizzi
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 2° | Introduction to 3D printing | D |
Franco Fummi
(Coordinator)
|
| 2° | Python programming language | D |
Carlo Combi
(Coordinator)
|
| 2° | HW components design on FPGA | D |
Franco Fummi
(Coordinator)
|
| 2° | Protection of intangible assets (SW and invention)between industrial law and copyright | D |
Roberto Giacobazzi
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 2° | Federated learning from zero to hero | D |
Gloria Menegaz
|
Natural Computing (2022/2023)
Teaching code
4S004557
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
Semester 1 dal Oct 3, 2022 al Jan 27, 2023.
Learning objectives
Knowledge and understanding The course is designed to first recall basic concepts of traditional computational models, such as formal languages and automata, and then present several models of bio-inspired computing, including bio-molecular algorithms. Main models of natural computing are presented, in terms of computational processes observed in and inspired by nature. Applying knowledge and understanding During the course students will aquire the following competences: Applying basic notions of discrete mathematics (sets, multisets, sequences, trees, graphs, induction, grammars and finite automata) to explain a few computational methods both to process genomic information and to investigate metabolic networks. Making judgements Students will develop the required skills in order to be autonomous in the following tasks: - choose and processing data in large genomic contexts; - choose the appropriate methodologies and tools for represent biological information in the context of discrete biological models. Communication skills The student will learn how to address the correct and appropriate methods and languages for communicating problems and solutions in the field of computationaql genomics and of biological dynamics. The course aims at developing the ability of the student both to master notions of discrete structures and dynamics, and to deepen his/her notion of Turing computation, in order to extend it to informational processes involving either natural or bio-inspired algorithms. Student's knowledge of all the topics explained in class will be tested at the exam, along with his/her learning and understanding skills. Lifelong learning skills Introduction to natural computing, biological algorithms, and life algorithmic strategies. Basic notions of discrete mathematics and of formal language theory (Chomsky's hierarchy, automata, and computability). Elements of information theory (information sources, codes, entropy, and entropy divergences, typical sequences, first and second Shannon's theory). Methods to extract and analyze genomic dictionaries. Genomic profiles and distributions of recurrent motifs. Software IGtools to analyze and visualize genomic data. Computational models of bio-molecular processes, such as DNA self-assembly and membrane computing. DNA computing and bio-complexity of bio-algorithms. DNA algorithms to solve NP-complete problems. MP grammars, networks, and metabolic dynamics.
Prerequisites and basic notions
Basic knowledge (provided by any bachelor degree in science) of: fundamentals of in-silico computation, discrete data structures, and algorithms
Program
The course provides students with knowledge on natural computational models, as computational processes both observed in nature and inspired by the functioning of natural systems. Namely, general knowledge on different natural and biological computational models will be given, with a focus on i) the design and implementation of bio-molecular algorithms (DNA computing), ii) cellular and metabolic distributed computation models, and iii) (alignment-free) methods to analyse genomic information.
Bibliography
Didactic methods
In class lectures
Learning assessment procedures
Oral examination (about one hour). Optional projects or seminars may be agreed to improve the final evaluation.
Evaluation criteria
Student's capability to communicate explained notions by means of an appropriate technical language (definitions, proofs, algorithms, bio-implementations, data analysis methods). Critical capability of comprehension and learning, development of theoretical and applied knowledge, and autonomy of the student will be evaluated as well.
Criteria for the composition of the final grade
Grade achieved at the oral exam may be incremented by by the evaluation of a project or seminar agreed between professor and student.
Exam language
either English or Italian
