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

A.A. 2023/2024 A.A. 2022/2023 A.A. 2021/2022 A.A. 2020/2021

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

ModulesCreditsTAFSSD
9
B
SECS-P/08
12
C
MAT/06
12
B
INF/01

2° Year  activated in the A.Y. 2024/2025

ModulesCreditsTAFSSD
Training
6
F
-
Final exam
22
E
-
activated in the A.Y. 2024/2025
ModulesCreditsTAFSSD
Training
6
F
-
Final exam
22
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
9
B/C
IUS/01 ,M-FIL/03
Between the years: 1°- 2°
1 module among the following
6
C
M-FIL/02
6
C
IUS/02
6
C
IUS/17
6
C
SPS/07
6
C
IUS/04
Between the years: 1°- 2°
2 courses among the following
6
C
FIS/02
6
C
MAT/09
6
C
AGR/01
6
C
MAT/06
6
C
MAT/08
6
C
FIS/02
Between the years: 1°- 2°
2 courses among the following (a.a. 2023/24: Statistical methods for business intelligence not activated)
6
B
SECS-P/10
6
B
SECS-P/08
6
B
SECS-P/10
6
B
SECS-P/08
6
B
SECS-S/01
Between the years: 1°- 2°
2 courses among the following
6
B
INF/01
6
B
ING-INF/05
6
B
ING-INF/05
6
B
INF/01
6
B
INF/01
Between the years: 1°- 2°
8
D
-

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.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Statistical learning - PART II  (2023/2024)

Teaching code

4S009067

Teacher

Alberto Castellini

Credits

3

Also offered in courses:

Language

English en

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Period

Semester 2 dal Mar 4, 2024 al Jun 14, 2024.

Courses Single

Authorized

Lessons timetable MoodleMoodle Seminars 0

To show the organization of the course that includes this module, follow this link:  Course organization

Program

Laboratory:
- Introduction to data analysis with Python
- Linear regression (Python)
- Variable and model selection in linear models (Python)
- Ridge and Lasso regularization for linear regression models (Python)
- Classification with logistic regression (Python)
- Data clustering with k-means and hierarchical approaches (Python)
- Artificial Neural Networks (Python)

Bibliography

Vai alla bibliografia
Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Didactic methods

Laboratory experiences, lectures, case study.
The rights of students will be preserved in situations of travel limitation or confinement due to national provisions to combat COVID or in particular situations of fragile health. In these cases, you are invited to contact the teacher directly to organize the most appropriate remedial strategies.

Learning assessment procedures

The exam consists of an oral test on the topics covered in the course and the related exercises carried out in the laboratory. In case of high participation, the oral exam will be replaced by a written exam with equivalent questions. Compatibly with the number of students, the delivery and presentation of a project or a scientific paper will be required.

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

Evaluation criteria

Theoretical and applied knowledge of the techniques taught in the course; critical ability to select techniques based on the problem; ability to use the techniques taught in the course.

Exam language

Inglese