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

2° Year   activated in the A.Y. 2020/2021

ModulesCreditsTAFSSD
6
B
MAT/05
Final exam
32
E
-
activated in the A.Y. 2020/2021
ModulesCreditsTAFSSD
6
B
MAT/05
Final exam
32
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
1 module between the following
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/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/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°
Other 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  (2019/2020)

Teaching code

4S008278

Teacher

Giacomo Albi

Coordinator

Giacomo Albi

Credits

6

Language

English en

Scientific Disciplinary Sector (SSD)

MAT/08 - NUMERICAL ANALYSIS

Period

II semestre dal Mar 2, 2020 al Jun 12, 2020.

Lessons timetable MoodleMoodle Seminars 1

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


- Computational linear algebra: SVD, Random matrices for low dimensional data, sparse recovery (compressed sensing, low rank matrix recovery, dictionary learning).

- Convex optimization (Stochastic gradient, ).

- Geometry of data analysis (ISOMAP, diffusion map, random graphs)


Reference texts
Author Title Publishing house Year ISBN Notes
Stephane Mallat A Wavelet Tour of Signal Processing (Edizione 2) Academic Press 1999 9780124666061
Avrim Blum, John Hopcroft, Ravi Kannan, Foundations of Data Science Cambridge University Press 2020
John A. Lee, Michel Verleysen Nonlinear Dimensionality Reduction Springer 2006
I.T. Jolliffe Principal Component Analysis Springer 2002

Examination Methods

The exam consists of written questions/exercises + oral examination. 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