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
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Mathematics - Enrollment from 2025/2026The 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 |
|---|
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
1 module between the following1 module between the following3 modules among the followingLegend | 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.
Foundation of data analysis (2021/2022)
Teaching code
4S008278
Teacher
Coordinator
Credits
6
Also offered in courses:
- Continuous optimization for data science of the course Master's degree in Data Science
Language
English
Scientific Disciplinary Sector (SSD)
MAT/08 - NUMERICAL ANALYSIS
Period
Primo semestre dal Oct 4, 2021 al Jan 28, 2022.
Learning outcomes
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.
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
Examination Methods
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.
