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.
1° Year
Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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1 module between the following
1 module between the following
3 modules among the following
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.
Foundation of data analysis (2019/2020)
Teaching code
4S008278
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
MAT/08 - NUMERICAL ANALYSIS
Period
II semestre dal Mar 2, 2020 al Jun 12, 2020.
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)
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.