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.
Academic calendar
The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.
Course calendar
The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..
Period | From | To |
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I semestre | Oct 1, 2024 | Jan 31, 2025 |
II semestre | Mar 3, 2025 | Jun 13, 2025 |
Exam calendar
Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.
Should you have any doubts or questions, please check the Enrollment FAQs
Academic staff
Study Plan
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.
1° Year
Modules | Credits | TAF | SSD |
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2° Year It will be activated in the A.Y. 2025/2026
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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.
Type D and Type F activities
Modules not yet included
Machine Learning for Data Science (2024/2025)
Teaching code
4S009069
Teacher
Credits
6
Also offered in courses:
- Machine Learning for Data Science of the course Master's degree in Data Science
Language
English
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Period
I semestre dal Oct 1, 2024 al Jan 31, 2025.
Courses Single
Authorized
Learning objectives
The course aims to provide the basic tools for machine learning, together with specific techniques to deal with large amounts of data, such as deep learning. Theory and techniques will be specifically addressed to data science issues with particular emphasis on data analysis. At the end of the course the student has to show to have acquired the following skills:
- knowledge of the main types of data (e.g. binaries, texts, sounds, etc.)
- understanding and capability to use the basic elements of descriptive statistics, elementary probability, linear algebra with elements of optimization and regularization
- knowledge of basic machine learning techniques (e.g. support vector machines, random forest, etc.)
- knowledge of basic deep learning techniques (e.g. convolutional neural network, long-short memory machines, etc.)
- knowledge of the basics of Natural Language Processing for, for example, sentiment analysis
- knowledge of the basic issues in the context of measurement and Regression measures, e.g., RMSE (Root Mean Square Error), MAE, Rsquared and adjusted Rsquared)
- knowledge of the basic tools in supervised training, e.g., confusion matrix, accuracy, precision, recall, F1, Curve precision-recall, ROC, average precision, CMC NLP: Bleu, Spice
Learning assessment procedures
The examination involves discussion with the lecturer of a project proposing a solution to an industrial problem.
The student must present his/her work in approximately 15 minutes (with or without the use of supporting material such as slides, written report, demo, etc.), followed by questions from the lecturer.
Evaluation criteria
For the composition of the grade, the following will be taken into account
- performance of the system developed (with different metrics from problem to problem);
- theoretical motivation that prompted the student to make the design choices;
- ability to set out the key points of the project clearly and concisely;
- ability to sustain a discussion on possible alternative solutions and potential causes of failure of the solution devised.
The student must also demonstrate mastery of all the topics in the syllabus (including those not covered in the project).
Career prospects
Module/Programme news
News for students
There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details: only in this way will you be able to receive notification of all the notices from your teachers and your secretariat via email and soon also via the Univr app.
Graduation
Deadlines and administrative fulfilments
For deadlines, administrative fulfilments and notices on graduation sessions, please refer to the Graduation Sessions - Science and Engineering service.
Need to activate a thesis internship
For thesis-related internships, it is not always necessary to activate an internship through the Internship Office. For further information, please consult the dedicated document, which can be found in the 'Documents' section of the Internships and work orientation - Science e Engineering service.
Final examination regulations
Upon completion of the Degree programme, students will need to submit and present their thesis/dissertation, which must be in English and focusing on a scientific topic covered during the programme. Alternatively, the thesis/dissertation may consist of the analysis and solution of a case study (theoretical and/or relevant to a real industrial context), experimental work, possibly developed as part of an internship, or original and independent research work that may include mathematical formalisation, computer design and a business-oriented approach.
These activities will be carried out under the guidance of a Thesis Supervisor at a University facility, or even outside the University of Verona, either in Italy or abroad, provided that they are recognised and accepted for this purpose in accordance with the teaching regulations of the Master's Degree programme in Data Science.
22 CFU credits shall be awarded for the final examination (assessment of the thesis/dissertation).
The Graduation Committee, which is in charge of the evaluation of the final examination (presentation of the dissertation in English) shall evaluate each candidate, based on their achievements throughout the entire degree programme, carefully assessing the degree of consistency between educational and professional objectives, as well as their ability for independent intellectual elaboration, critical thinking, communication skills and general cultural maturity, in relation to the objectives of the Master's Degree programme in Data Science, and in particular, in relation to the topics dealt with by the candidate in their thesis.
Students may take the final exam only after they have passed all the other modules and exams that are part of their individual study plan, and fulfil all the necessary administrative requirements, in accordance with the terms indicated in the General Study Manifesto.
The graduation exam and ceremony will be carried out by the Graduation Committee appointed by the Chair of the Teaching Committee and composed of a President and at least four other members chosen among the University's lecturers.
The thesis/dissertation will be assessed by the Dissertation Committee, which is composed of three lecturers possibly including the Thesis Supervisor, and appointed by the Chair of the Teaching Committee. The Dissertation Committee shall produce an evaluation of the dissertation, which will be submitted to the Graduation Committee, which will issue the final graduation mark. The Teaching Committee shall govern the procedures of the Dissertation Committee and the Graduation Committee, and any procedures relating to the score awarded for the final exam through specific regulations issued by the Teaching Committee.
Documents
Title | Info File |
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Regulations for the final exame | pdf, it, 326 KB, 19/03/24 |
Modalità di frequenza
Come riportato nel Regolamento Didattico, la frequenza al corso di studio non è obbligatoria.