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.

Study Plan

A.A. 2024/2025 A.A. 2023/2024 A.A. 2022/2023 A.A. 2021/2022 A.A. 2020/2021 A.A. 2019/2020 A.A. 2018/2019 A.A. 2017/2018 A.A. 2016/2017

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 2025/2026

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.

activated in the A.Y. 2022/2023
ModulesCreditsTAFSSD
3 courses among the following
6
B
INF/01
6
B
INF/01
6
B
INF/01
6
B
INF/01
6
B
INF/01
6
B
INF/01
6
B
ING-INF/05
Further linguistic skills (C1 English suggested)
3
F
-
Stages
3
F
-
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
12
D
-

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Data Analysis for Biomedical Sciences  (2021/2022)

Teaching code

4S010400

Teacher

Gloria Menegaz

Coordinator

Gloria Menegaz

Credits

2

Also offered in courses:

Language

Italian

Scientific Disciplinary Sector (SSD)

NN - -

Period

Primo semestre dal Oct 4, 2021 al Jan 28, 2022.

Lessons timetable MoodleMoodle Seminars 0

Learning outcomes

The course aims to provide some basic knowledge of machine learning methods and models for the analysis of biomedical data with emphasis on aspects related to explainability (XAI). At the end of the course the student will have to demonstrate the ability to design and conduct a study of biomedical data, from their analysis to the application of models aimed at solving the starting question/thesis.

Program

Theory:
1. Introduction to the landscape of biomedical data analysis
2. Visualization and representations of relationships through the main graphical representations
3. Data modeling through the main prediction and classification models used in the biomedical field
4. Presentation of the solution through the evaluation of the model performance
5. Basic explainable artificial intelligence (XAI) methods for interpreting results
Laboratory:
In order to provide students with the practical basis for managing simple studies of biomedical data, great importance will be given to the laboratory part in which a specific problem to be solved will be presented and addressed. During this phase, the code that allows you to solve the problem will first be presented and commented on, and then some exercises will be proposed to solve in the remaining time. The exercises will be carried out in Python.

Bibliography

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Examination Methods

Interview to discuss the project assigned individually or to groups in the laboratory.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE