## 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, 2018 | Jan 31, 2019 |

II semestre | Mar 4, 2019 | Jun 14, 2019 |

Session | From | To |
---|---|---|

Sessione invernale d'esame | Feb 1, 2019 | Feb 28, 2019 |

Sessione estiva d'esame | Jun 17, 2019 | Jul 31, 2019 |

Sessione autunnale d'esame | Sep 2, 2019 | Sep 30, 2019 |

Session | From | To |
---|---|---|

Sessione Estiva | Jul 17, 2019 | Jul 17, 2019 |

Sessione Autunnale | Nov 20, 2019 | Nov 20, 2019 |

Sessione Invernale | Mar 17, 2020 | Mar 17, 2020 |

Period | From | To |
---|---|---|

Sospensione dell'attività didattica | Nov 2, 2018 | Nov 3, 2018 |

Vacanze di Natale | Dec 24, 2018 | Jan 6, 2019 |

Vacanze di Pasqua | Apr 19, 2019 | Apr 28, 2019 |

Festa del Santo Patrono | May 21, 2019 | May 21, 2019 |

Vacanze estive | Aug 5, 2019 | Aug 18, 2019 |

## 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

Ugolini Simone

simone.ugolini@univr.itZoppello Marta

## 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 |
---|

Mathematical analysis 1

Computer Architecture

2° Year activated in the A.Y. 2019/2020

Modules | Credits | TAF | SSD |
---|

3° Year activated in the A.Y. 2020/2021

Modules | Credits | TAF | SSD |
---|

Modules | Credits | TAF | SSD |
---|

Mathematical analysis 1

Computer Architecture

Modules | Credits | TAF | SSD |
---|

Modules | Credits | TAF | SSD |
---|

#### 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**

### Probability and Statistics (2018/2019)

Teaching code

4S02843

Credits

6

Language

Italian

Also offered in courses:

- Probability and Statistics - Teoria of the course Bachelor's degree in Bioinformatics
- Probability and Statistics - Laboratorio of the course Bachelor's degree in Bioinformatics

Scientific Disciplinary Sector (SSD)

MAT/06 - PROBABILITY AND STATISTICS

The teaching is organized as follows:

##### Teoria

##### Laboratorio [Bioinformatica]

##### Laboratorio [Informatica]

## Learning outcomes

The course aims at providing the fundamental concepts of descriptive statistics and probability, with the task of modeling real problems by means of probability methods and applying to real problems statistic techniques.

At the end of the course the student will have to demonstrate to understand the main statistical techniques for describing problems;

to be able to interpret results of statistical analyses; to be able to develop know-how necessary to continue the study autonomously in the context of statistical analysis.

## Program

------------------------

MM: Theory

------------------------

(1) Descriptive Statistics. Describing data sets (frequency tables and graphs). Summarizing data sets (sample mean, median, and mode, sample variance and standard deviation, percentiles and box plots). Normal data sets. Sample correlation coefficient.

(2) Probability Theory. Elements of probability: sample space and events, Venn diagrams and the algebra of events, axioms of probability, sample spaces having equally likely outcomes, conditional probability, Bayes’ formula, independent events. Random variables and expectation: types of random variables, expected value and properties, variance, covariance and variance of sums of random variables. Moment generating functions. Weak law of large numbers. Special random variables: special random variables and distributions arising from the normal (chi-square, t, F).

(3) Statistical Inference. Distributions of sampling statistics. Parameter estimation (maximum likelihood estimators, interval estimates). Hypothesis testing and significance levels.

(4) Regression. Least squares estimators of the regression parameters. Distribution of the estimators. Statistical inferences about the regression parameters. The coefficient of determination and the sample correlation coefficient. Analysis of residuals: assessing the model. Transforming to linearity. Weighted least squares. Polynomial regression and multiple linear regression.

------------------------

MM: Laboratory

------------------------

The course includes a series of laboratories in the computer lab with exercises in MATLAB environment. The exercises will cover an introduction to MATLAB and the main functions and tools useful for statistics, for the generation of random variables and the analysis of random data samples. The laboratories complement lectures by consolidating learning and developing problem-solving and hands-on practical skills.

Teaching methods: lectures, class exercises and laboratory exercises. Educational material (powerpoint file) will be available on the eLearning platform.

## Bibliography

Activity | Author | Title | Publishing house | Year | ISBN | Notes |
---|---|---|---|---|---|---|

Teoria | Sheldon M. Ross | Probabilità e Statistica per l'ingegneria e le scienze, Apogeo Education, terza edizione, 2015, ISBN: 978-88-916-0994-6 (Edizione 3) | Apogeo Education | 2015 | 978-88-916-0994-6 |

## Examination Methods

Written exam consisting of theoretical questions, problems, and laboratory questions.

To pass the exam, the students must show that:

- they have understood the basic concepts of probability theory and statistics;

- they are able to use the knowledge acquired during the course to solve the assigned problem;

- they are able to program in MATLAB environment in the statistical and probabilistic context.

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

## 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

## List of theses and work experience proposals

theses proposals | Research area |
---|---|

Analisi e percezione dei segnali biometrici per l'interazione con robot | AI, Robotics & Automatic Control - AI, Robotics & Automatic Control |

Integrazione del simulatore del robot Nao con Oculus Rift | AI, Robotics & Automatic Control - AI, Robotics & Automatic Control |

Domain Adaptation | Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Computer graphics, computer vision, multi media, computer games |

Domain Adaptation | Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video) |

BS or MS theses in automated reasoning | Computing Methodologies - ARTIFICIAL INTELLIGENCE |

Domain Adaptation | Computing Methodologies - IMAGE PROCESSING AND COMPUTER VISION |

Domain Adaptation | Computing methodologies - Machine learning |

Dati geografici | Information Systems - INFORMATION SYSTEMS APPLICATIONS |

Analisi e percezione dei segnali biometrici per l'interazione con robot | Robotics - Robotics |

Integrazione del simulatore del robot Nao con Oculus Rift | Robotics - Robotics |

BS or MS theses in automated reasoning | Theory of computation - Logic |

BS or MS theses in automated reasoning | Theory of computation - Semantics and reasoning |

Proposte di tesi/collaborazione/stage in Intelligenza Artificiale Applicata | Various topics |

Proposte di Tesi/Stage/Progetto nell'ambito dell'analisi dei dati | Various topics |

## Attendance

As stated in the Teaching Regulations for the A.Y. 2022/2023, attendance at the course of study is not mandatory.