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 |
---|---|---|
I semestre | Oct 1, 2020 | Jan 29, 2021 |
II semestre | Mar 1, 2021 | Jun 11, 2021 |
Session | From | To |
---|---|---|
Sessione invernale d'esame | Feb 1, 2021 | Feb 26, 2021 |
Sessione estiva d'esame | Jun 14, 2021 | Jul 30, 2021 |
Sessione autunnale d'esame | Sep 1, 2021 | Sep 30, 2021 |
Session | From | To |
---|---|---|
Sessione Estiva | Jul 15, 2021 | Jul 15, 2021 |
Sessione Autunnale | Oct 15, 2021 | Oct 15, 2021 |
Sessione Invernale | Mar 15, 2022 | Mar 15, 2022 |
Period | From | To |
---|---|---|
Festa dell'Immacolata | Dec 8, 2020 | Dec 8, 2020 |
Vacanze Natalizie | Dec 24, 2020 | Jan 3, 2021 |
Epifania | Jan 6, 2021 | Jan 6, 2021 |
Vacanze Pasquali | Apr 2, 2021 | Apr 5, 2021 |
Santo Patrono | May 21, 2021 | May 21, 2021 |
Festa della Repubblica | Jun 2, 2021 | Jun 2, 2021 |
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
2° Year activated in the A.Y. 2021/2022
Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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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.
Data Mining and Knowledge Discovery (2020/2021)
Teaching code
4S008911
Teacher
Coordinator
Credits
6
Language
Italian
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
I semestre dal Oct 1, 2020 al Jan 29, 2021.
Learning outcomes
The corse aims to provide the theoretical and practical foundations for integrating data from, possibly, heterogeneous sources and the subsequent phase of extraction of summary information/knowledge. By completing the course, the students will be able to tackle complex data mining problems by designing and implementing a full pipeline that allows its user to integrate the necessary data sources, select and apply the adequate data mining techniques for solving a specific data mining problem, and evaluate its performances. Given a data mining problem, coming from a real-world domain ranging from industry to healthcare, the course enables the students to design, apply and test original solutions or or modifications of existing ones, for solving it and evaluate the feasibility of the proposed solution in a real environment.
Program
Functional Dependencies (FD):
concepts and applications of FDs, forcing and verifying FDs in PostgreSQL
Approximate Functional Dependencies (AFD):
introducing approximation in FDs as confidence measure. Knowledge extraction using AFD: examples. AFD analysis.
Algorithms for extracting AFDs:
minimal AFDs: definition, semantics and analysis. Theoretical Lower Bounds on the number of minimal AFD: the curse of cardinality. Basic algorthm for extracting minimal AFD. Compact representations of
sets of extracte AFDs. Randomized algorithms for extracting minimal AFDs:
theory and implementation.
Approximation in presence of measures:
Delta Functional Dependencies (DFDs) : definition, application, and verification. Analysis of DFDs extracted from the biomedical domain. Approximated DFDs
(ADFD):
definition, applications and analysis in the biomedical domain (examples). Algorithm for verifying single ADFD restricted to the case of 2 measures (2ADFD):
complexity, implementation. Extraction of minimal 2ADFD from data.
Association Rules (ARs):
definition, examples in the biomedical domain. Extraction of di AR: support and confidence. Theoretical analysis: the curse of cardinality. Frequent Itemsets (FIs): definition, role in the extraction
of ARs, and algorithm for vandidates generation. ARs extraction from sets of FIs. Sets of FIs: minimal sets, closed sets.
Strategies for exploring FIs lattices. Alternatives to standard extraction algorithm using specific data structures (hash trees, FP-trees). Evaluation of association patterns: drawbacks of the support/confidence framework. Examples of paradoxes. alternative measures for association pattern analysis:
definition and examples.
Extraction Transformation and Loading (ETL):
definition, functions, role inside a data warehouse, data flows. Basic entities of ETL procedures and how they work: Job, Transformations, Job, Step, Transformation Step. Conceptual modelling of ETL procedures in Business Process Model and Notation (BPMN). Modelling examples: case studies. Embedding external procedures into ETL procedures: comunication, staging and managing of errors. API (Application Programming Interface) usage inside ETL procedures. Short description of XPATH constructs and how to use them. Screen scraping of websites in ETL procedures by using XPATH. Using Business Intellingence tools to realize ETL procedures.
Entropy-based classifiers:
introduction to the concept of Entropy. Decision Trees in the biomedical context. The Iterative Dichotomiser 3 (ID3) classifier: algorithm, examples and implementation. Measures discretization. Using ID3 for discretizing measures:
problems, modification and implementation. Temporal analysis applications.
Reporting and OLAP (Online Analytical Processing):
Interactive reporting systems: querying large databases, parametrization of the reports. Dynamic retrieval of report information by using ETL transformations. Modelling analysis using OLAP cubes and their implementation: case studies. Using Business Intellingence tools to realize dynamic/interactive reports and OLAP cubes
Distributed Data Mining:
elements of distributed computing, split a data mining problem for solving it in a distributed fashion,
model and implement a ditributed system for data mining. How to use NoSQL databases for
distributed computations.
Probabilistic Analysis of Processes:
Qualitative analysis of a process using process mining and process discovery
techniques. Extraction and trasformation of processes into
probabilistic models (Markov Chains, Markov Decision Processes).
Tools for probabilistic analysis of systems (PRISM model checker).
SUGGESTED TEXTS:
DJ Hand, H Mannila, P Smyth
Principles of data mining
MIT Press Cambridge, MA, USA ©2001
ISBN:0-262-08290-X 9780262082907
Roland Bouman, Jos van Dongen
Pentaho Solutions: Business Intelligence and Data Warehousing with Pentaho and MySQL
Wiley Publishing, Inc.
ISBN: 978-0-470-48432-6
648 pages
September 2009
The elements of statistical learning. Data mining, inference, and prediction.
T. Hastie, R. Tibshirani, J. Friedman.
2009 Springer
COURSE MATERIAL:
class slides;
example data (in .csv format) for completing the exercises proposed during classes;
implementation of the procedures introduced during the course;
Jupyter notebooks and docker containers for easily run the algorithm explained during the lectures.
Author | Title | Publishing house | Year | ISBN | Notes |
---|---|---|---|---|---|
Roland Bouman, Jos van Dongen | Pentaho Solutions: Business Intelligence and Data Warehousing with Pentaho and MySQL | Wiley Publishing, Inc. | 2009 | 978-0-470-48432-6 | |
DJ Hand, H Mannila, P Smyth | Principles of data mining | MIT Press Cambridge | 2001 | 9780262082907 | |
T. Hastie, R. Tibshirani, J. Friedman. | The elements of statistical learning. Data mining, inference, and prediction. (Edizione 2) | Springer | 2009 |
Examination Methods
The exam modality aims to verify the autonomy and the skills of the student in applying the concepts provided during the course for realizing a full end-to-end pipeline for a given Data Mining problem.
The exam consists of an interview on the implementation
of two projects assigned during classes, one for each macro-topic of the course:
1) ETL and OLAP Analysis
2) Data Mining;
The two projects must be realized as a team or as an individual. Moreover, a necessary but not sufficient condition for passing the exam is that both the implementations of the projects must be complete. In particular, each project will be evaluated on a scale going from 1 to 15 included, the final grade is given by the sum of the two individual project grades.
There is no difference in the exam modality among students that attended the course and students that did not.
Type D and Type F activities
Le attività formative in ambito D o F comprendono gli insegnamenti impartiti presso l'Università di Verona o periodi di stage/tirocinio professionale.
Nella scelta delle attività di tipo D, gli studenti dovranno tener presente che in sede di approvazione si terrà conto della coerenza delle loro scelte con il progetto formativo del loro piano di studio e dell'adeguatezza delle motivazioni eventualmente fornite.
years | Modules | TAF | Teacher |
---|---|---|---|
1° 2° | Matlab-Simulink programming | D |
Bogdan Mihai Maris
(Coordinator)
|
1° 2° | Programming Challanges | D |
Romeo Rizzi
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
1° 2° | Introduction to 3D printing | D |
Franco Fummi
(Coordinator)
|
1° 2° | Python programming language | D |
Vittoria Cozza
(Coordinator)
|
1° 2° | HW components design on FPGA | D |
Franco Fummi
(Coordinator)
|
1° 2° | Rapid prototyping on Arduino | D |
Franco Fummi
(Coordinator)
|
1° 2° | Protection of intangible assets (SW and invention)between industrial law and copyright | D |
Roberto Giacobazzi
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
1° 2° | The fashion lab (1 ECTS) | D |
Maria Caterina Baruffi
(Coordinator)
|
1° 2° | The course provides an introduction to blockchain technology. It focuses on the technology behind Bitcoin, Ethereum, Tendermint and Hotmoka. | D |
Nicola Fausto Spoto
(Coordinator)
|
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
List of theses and work experience proposals
Attendance
As stated in the Teaching Regulations for the A.Y. 2022/2023, attendance at the course of study is not mandatory.