Training and Research

PhD school courses/classes - 2024/2025

Please note: Additional information will be added during the year. Currently missing information is labelled as “TBD” (i.e. To Be Determined).

1. PhD students must obtain a specified number of CFUs each year by attending teaching activities offered by the PhD School.
First and second year students must obtain 8 CFUs. Teaching activities ex DM 226/2021 provide 5 CFUs; free choice activities provide 3 CFUs.
Third year students must obtain 4 CFUs. Teaching activities ex DM 226/2021 provide 2 CFUs; free choice activities provide 2 CFUs.
More information regarding CFUs is found in the Handbook for PhD Students: https://www.univr.it/phd-vademecum

2. Registering for the courses is not required unless explicitly indicated; please consult the course information to verify whether registration is required or not. When registration is actually required, instructions will be sent well in advance. No confirmation e-mail will be sent after signing up. Please do not enquiry: if you entered the requested information, then registration was silently successful.

3. When Zoom links are not explicitly indicated, courses are delivered in presence only.

4. All information we have is published here. Please do not enquiry for missing information or Zoom links: if the information you need is not there, then it means that we don't have it yet. As soon as we get new information, we will promptly publish it on this page.

Summary of training activities

Teaching Activities ex DM 226/2021: Linguistic Activities

Teaching Activities ex DM 226/2021: Research management and Enhancement

Teaching Activities ex DM 226/2021: Statistics and Computer Sciences

Teaching Activities: Free choice

THE EMPIRICAL PHENOMENOLOGICAL METHOD (EPM): THEORETICAL FOUNDATION AND EMPIRICAL APPLICATION IN EDUCATIONAL AND HEALTHCARE FIELDS

Credits: 2

Language: English

Teacher:  Luigina Mortari

DOTTORATO E MERCATO DEL LAVORO: WORKSHOP FORMATIVI PER DOTTORANDI E NEO-DOTTORI DI RICERCA

Credits: 4

Language: Italian
ARE YOU SURE YOU CAN DEFEAT A CHATBOT?

Credits: 1

Language: Italian
MEETING UKRAINE: THE IMPACT OF WAR AND FUTURE OPPORTUNITIES

Credits: 1

Language: Italian
OPEN SCIENCE: THE MIGHTY STICK AGAINST "BAD" SCIENCE

Credits: 2

Language: English

Teacher:  Michele Scandola

Differential diagnosis of demyelinating diseases of the central nervous system

Credits: 2

Language: Italiano; English

Teacher:  Alberto Gajofatto

EMOTIONS, BELIEFS, AND SKILLS TO FACE CLIMATE CHANGE AND EMBRACE CLIMATE ACTION

Credits: 0.5

Language: English

Teacher:  Isolde Martina Busch

COMPUTATIONAL MECHANISMS UNDERLYING SENSORIMOTOR LEARNING

Credits: 4.5

Language: English

Teacher:  Matteo Bertucco

CSF DYNAMICS: ANATOMICAL AND FUNCTIONAL FEATURES

Credits: 0.5

Language: English

Teacher:  Alberto Feletti

sleep related disoders: focus on REM and NREM parasomnia and SR movement disorders

Credits: 1.5

Language: italiano o inglese

Teacher:  Elena Antelmi

Tecniche di immagine per l'analisi della composizione corporea

Credits: 1

Language: Inglese/English

Teacher:  Carlo Zancanaro

Tecniche di ricerca in neuroscienze: misurare e modulare l'attività neuronale

Credits: 2.3

Language: non prevista

Teacher:  Giuseppe Busetto

Introduction to Probability (module II)  (2024/2025)

Teacher

Marco Minozzo

Referent

Marco Minozzo

Credits

1

Language

English

Class attendance

Free Choice

Location

VERONA

MoodleMoodle Seminars 0

Learning objectives

The purpose of the modules is to explain, at an intermediate level, the basis of probability theory and some of its more relevant theoretical features.
The topics will be illustrated and explained through many examples.
Students are expected to acquire the language and the concepts needed to better understand the probabilistic models and the statistical techniques used in their subjects.

Prerequisites and basic notions

There are no particular learning requirements. Students should have already been introduced (though at an elementary level) to probability and statistics. Students should also have some confidence in elementary set theory and mathematical calculus.

Program

- Discrete random variables, binomial distribution.
- Continuous random variables, density functions.
- Transformations of random variables.
- Expectation, variance and moments of random variables.
- Multidimensional random variables, discrete multidimensional random variables, marginal and conditional distributions, independent random variables.
- Linear combinations of random variables.
- Introduction to limit theorems, weak law of large numbers.

Bibliography

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Didactic methods

Lessons will be delivered via Zoom; recordings will be made available by the lecturer. Attendance is not required, but passing a written test is required to obtain credits.

Learning assessment procedures

The final assessment will be through a written paper. Alternatively, there will be a Moodle QUIZ.

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

Scheduled Lessons

When Classroom Teacher topics
Wednesday 19 February 2025
09:00 - 12:00
Duration: 3:00 AM 		Aula virtuale - Lezione online Marco Minozzo Revision on random variables: discrete and continuous, density function, cumulative distribution function. Bernoulli and Gaussian distribution. Transformations of random variables. Expectation of a random variable.
Thursday 20 February 2025
14:00 - 17:00
Duration: 3:00 AM 		Aula virtuale - Lezione online Marco Minozzo Examples of expectations of random variables. Expectation of the Binomial and Gaussian distribution. Expectation of a transformation. Properties of expectation: linearity. Example of a mixture of random variables. Variance: definition and properties. Standardized random variable. Moments.
Friday 21 February 2025
14:00 - 16:00
Duration: 2:00 AM 		Aula virtuale - Lezione online Marco Minozzo Quantiles. K-dimensional random variables. Discrete k-dimensional random variables: joint distributions, marginal distributions, conditional distributions. Independent random variables: definitions and properties.