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

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.

CURRICULUM TIPO:

1° Year 

ModulesCreditsTAFSSD
6
B
MAT/07
6
B
MAT/03
12
B
MAT/05
6
B
MAT/05
6
B
MAT/06

2° Year   It will be activated in the A.Y. 2025/2026

ModulesCreditsTAFSSD
6
B
MAT/05
Final exam
32
E
-
It will be activated in the A.Y. 2025/2026
ModulesCreditsTAFSSD
6
B
MAT/05
Final exam
32
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
1 module between the following:
- A.A. 2024/2025 Computational algebra not activated;
- A.A. 2025/2026 Homological Algebra not activated.
6
B
MAT/02
6
B
MAT/02
Between the years: 1°- 2°
1 module between the following
6
B
MAT/08
6
B
MAT/08
Between the years: 1°- 2°
3 modules among the following
6
C
MAT/03
6
C
MAT/02 ,MAT/03
6
C
MAT/02
6
C
MAT/08
6
C
MAT/08
6
C
MAT/02
6
C
MAT/06
6
C
INF/01
6
C
MAT/05
6
C
MAT/09
6
C
MAT/02
6
C
MAT/08
6
C
MAT/08
6
C
MAT/07
6
C
INF/01 ,MAT/06
Between the years: 1°- 2°
12
D
-
Between the years: 1°- 2°
Further activities
4
F
-

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

Foundation of data analysis  (2024/2025)

Teaching code

4S008278

Teacher

Giacomo Albi

Coordinator

Giacomo Albi

Credits

6

Also offered in courses:

Language

English en

Scientific Disciplinary Sector (SSD)

MAT/08 - NUMERICAL ANALYSIS

Period

Semester 1  dal Oct 1, 2024 al Jan 31, 2025.

Courses Single

Authorized

Lessons timetable MoodleMoodle Seminars 2

Learning objectives

After successful completion of the module students will be able to understand and apply the basic notions, concepts, and methods of computational linear algebra, convex optimization and differential geometry used for data analysis. In particular, they will master the use of singular value decomposition method as well as random matrices for low dimensional data representations, including fundamentals of sparse recovery problems, as e.g., compressed sensing, low rank matrix recovery, and dictionary learning algorithms. The students will be also able to manage the representation of data as clusters around manifolds in high dimensions and in random graphs, acquiring methods to construct local charts and clusters for the data. In complementary laboratory sessions they will get acquainted with suitable programming tools and environment in order to analyse relevant case studies.

To pass the exam, students must demonstrate:
- to have understood the principles underlying computational linear algebra, convex optimization and differential geometry applied to data analysis.
- to be able to present arguments on the topics of the course in a precise and organic way without digressions
- to know how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions and projects.

Prerequisites and basic notions

Differential calculus, linear algebra, basics of numerical analysis, basic knowledge of a programming language

Program

* Introduction to optimization
- optimality conditions
- numerical methods
* Singular Value Decomposition:
- Best k-rank approximation, Randomized SVD
- Principal Component Analysis, Pseudo-Inverse.
* Compressed Sensing
- Basis pursuit problem: l1-minimization and sparse recovery
- Application to signals and images reconstruction.
* Data Analysis
- Dimensionality reduction techniques: (Local Linear Embedding, ISOMAP, diffusion map).
- Supervised learning for classification: Support Vector Machine
- Unsupervised learning for clustering: K-means.
- Artificial Neural Networks and applications.

Bibliography

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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

* Lectures and homeworks
* Code development and examples
* Individual or group projects
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

To pass the exam, students must demonstrate:
- to have understood the principles underlying computational linear algebra, convex optimization and differential geometry applied to data analysis.
- to be able to present arguments on the topics of the course in a precise and organic way without digressions
- to know how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions and projects.
The exam consists of an oral examination with written questions and discussion. The development of a project is encouraged (but not mandatory) as an integration of the oral examination.

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

The student must be able to formalize and solve data analysis problems by using, adapting and developing advanced numerical methods in the context of optimization, numerical linear algebra seen during teaching.

Criteria for the composition of the final grade

Oral exam with questions (85%), resolution of exercises and home project (15%)

Exam language

Inglese