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 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° | Attention Laboratory | D |
Pietro Sala
(Coordinator)
|
| 1° 2° | Elements of Cosmology and General Relativity | D |
Claudia Daffara
(Coordinator)
|
| 1° 2° | Introduction to quantum mechanics for quantum computing | D |
Claudia Daffara
(Coordinator)
|
| 1° 2° | Introduction to smart contract programming for ethereum | D |
Sara Migliorini
(Coordinator)
|
| 1° 2° | Python programming language [English edition] | D |
Carlo Combi
(Coordinator)
|
| 1° 2° | Mini-course on Deep Learning & Medical Imaging | D |
Vittorio Murino
(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° | Attention Laboratory | D |
Pietro Sala
(Coordinator)
|
| 1° 2° | LaTeX Language | D |
Enrico Gregorio
(Coordinator)
|
| 1° 2° | Python programming language [Edizione in italiano] | D |
Carlo Combi
(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)
|
Machine learning for biological structures and networks (2024/2025)
Teaching code
4S009831
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Courses Single
Authorized
The teaching is organized as follows:
Teoria
Laboratorio
Learning objectives
The course is aimed at providing the theoretical and applicative basis of Pattern Recognition techniques for the computational analysis of biological objects with a complex structure (such as graphs, sequences, networks, strings and so on). In particular, the course introduces and discusses the most important computational techniques for the analysis of structured data, with particular emphasis on the representation and on the generative and discriminative approaches. Knowledge and understanding: At the end of the course, the student has to demonstrate to be able to apply to real data the methodologies for recognition of complex data, by developing a Pattern Recognition system. Applying knowledge and understanding: a) Representation of biological data with complex structure b) Classification of biological data with complex structure c) Clustering of biological data with complex structure Making judgements: At the end of the course, the student should demonstrate to be able to propose in an autonomous way efficient solutions for a given biomedical and bioinformatics domain, being able to identify critical issues linked to complex bioinformatics problems. Communication: At the end of the course, the tudent should demonstrate to be able to interact with colleagues in work groups. Lifelong learning skills: At the end of the course, the student should demonstrate to be able to learn and autonomously apply novel methodologies for facing bioinformatics and clinical problems. In particular, the student should demonstrate to be able to analyse a biological problem, involving complex and structured biological data, from a Pattern Recognition perspective; he will also have the skills needed to study, invent, develop and implement the different components of a Pattern Recognition System for biological structured data. The student will also be able to autonomously proceed with further Pattern Recognition studies.
Prerequisites and basic notions
Theory: basic notions on Pattern Recognition (a brief recap will be given at the beginning of the course), Basic notions of Algorithms, Probability, Statistics, Algebra.
Lab: Programming skills, Programming language used: Matlab (there will be an introductory lecture for students who are not familiar with Matlab)
Program
CHAPTER 1 Basic Pattern Recognition concepts and introduction to structured data
CHAPTER 2. Representation of structured data
- The Bag of words representation
- The dissimilarity-based representation
- Dimensionality reduction
- Learning representation with Neural Networks
CHAPTER 3. Models for structured data
- Generative models
- Bayes Networks
- Learning and inference
CHAPTER 4. Kernels for structured data
- Support Vector Machines e kernel
- Kernels for structured data
CHAPTER 5. Advances Learning paradigms
The course also contains a lab part, where algorithms seen during the theory part will be implemented and deeply analysed
Bibliography
Didactic methods
In person lectures plus in person lab sessions
Learning assessment procedures
The exam is aimed at the verification of the following skills:
- capability of clearly and concisely describe the different components of a Pattern Recognition System for structured data
- capability of analise, understand and describe a Pattern Recognition system (or a given part of it) relative to a biological problem which involves structured data
The exam consists of two parts
i) a written exam containing questions on topics presented during the course plus an exercise of "code understanding", for the lab part (15 points available). The written part is passed is the grade is greater or equal to 9.
ii) an oral presentation of a scientific paper published in relevant bioinformatics journals or conferences on a given argument (decided during the course). The paper is chosen by the candidate and approved by the instructor (15 points available).
The two parts of the exam can be passed separately; every part is passed if the grade is larger or equal to 9. The total exam is passed when both parts are passed: the final grade is the sum of the two grades. The evaluation of each part is maintained valid for the whole academic year.
Evaluation criteria
For the written part:
- Understanding of the questions and knowledge of related theoretical topic
- Clarity and precision of the used language
For the oral part:
- capability of choosing a scientific paper which is relevant with respect to the assigned topic
- Capability of understanding the methodologies and the results presented in the paper
- Capabilitiy of summarizing the paper in a conference-like talk
- Capability of rasining the interest of participants and clarity of exposition
Criteria for the composition of the final grade
The final grade is the sum of the two grades.
Exam language
English
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