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
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:
Bachelor's degree in Bioinformatics - Enrollment from 2025/2026The 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.
1° Year
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Linear algebra and analysis
2° Year activated in the A.Y. 2025/2026
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3° Year It will be activated in the A.Y. 2026/2027
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| Modules | Credits | TAF | SSD |
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Linear algebra and analysis
| Modules | Credits | TAF | SSD |
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1 module among the following (Discrete Biological Models 2nd year, other modules 3rd year)1 module among the following (Elements of physiology and Biophysics 2nd year, Model organism in biotechnology research and Molecular biology laboratory 3rd year)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.
Advanced machine learning techniques for biomedical data (2026/2027)
Teaching code
4S012348
Academic staff
Coordinator
Credits
6
Language
Italian
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
II semestre dal Mar 1, 2027 al Jun 11, 2027.
Courses Single
Authorized
Learning objectives
The course intends to provide the main theoretical and application concepts of some advanced machine learning techniques for the analysis and management of biological data, with particular emphasis on biomedical image. The problems and techniques related to the development of unsupervised Pattern Recognition systems will be presented and discussed, such as clustering, anomaly detection and similar. The main problems related to the processing and analysis of clinical studies based on diagnostic imaging will also be addressed, as well as the fundamental algorithms for resolving such situations such as image segmentation, registration and compression. After the course, students will be able to analyze a large class of unsupervised biological problems, using the Pattern Recognition point of view, and will have the necessary knowledge to design, develop and manage the different components of a pattern recognition system in real-world biomedical data contexts. They will also be able to use the image processing algorithms seen in class to solve typical problems that arise in clinical studies based on diagnostic imaging, by applying state-of-the-art methodologies and software.
Prerequisites and basic notions
Basic knowledge of Pattern Recognition and signal and image processing.
Program
The course is divided into two parts:
(1) Advanced Pattern Recognition
In this part, the fundamentals, issues and techniques of unsupervised pattern recognition will be discussed, with particular reference to clustering. In detail:
- Unsupervised pattern recognition: introduction to clustering, issues, applications
- Similarity measures
- Partitioned clustering methods: BSAS, K-means (and variants), Gaussian mixtures
- Hierarchical clustering methods
- Clustering validation
- Notes on advanced topics of unsupervised PR
(2) Advanced signal and image processing
- Geometric transformations and image alignment
- Image segmentation
- Image compression
Bibliography
Didactic methods
In person lectures
Learning assessment procedures
The exam consist of a written exam containing questions on topics presented during the two parts of the course
Evaluation criteria
- Understanding of the questions and knowledge of related theoretical topic
- Clarity and precision of the used language
Criteria for the composition of the final grade
The final grade will be calculated by taking the arithmetic mean of the grades obtained in each of the two parts of the course.
Exam language
Italiano
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