Anno Accademico 2021/2022

  • Docente: Valeria Simoncini
  • Crediti formativi: 6
  • SSD: MAT/08
  • Lingua di insegnamento: Inglese
  • Moduli: Valeria Simoncini (Modulo 1) Margherita Porcelli (Modulo 2)
  • Modalità didattica: Convenzionale - Lezioni in presenza (Modulo 1) Convenzionale - Lezioni in presenza (Modulo 2)
  • Campus: Bologna
  • Corso: Laurea Magistrale in Matematica (cod. 5827)

Conoscenze e abilità da conseguire

At the end of the course, students have theoretical and computational knowledge on matrix and tensor techniques for analysing large amounts of data. In particular, students are able to examine large samples of discrete data and extract interpretable information of relevance in image and data processing, in medical and scientific applications, and in social and security sciences.


The course presents fundamental matrix and tensor techniques commonly employed in Data Mining methods, together with optimization strategies designed to handle constrained problems typically arising in data science.

Mathematical tools will be developed for the following topics:

  • Clustering strategies (hierarchical, spectral, subspace-based, tec)
  • Compression and approximation strategies (PCA, non-negative factorizations, etc)
  • Dictionary learning
  • Matrix completion

Computational experience in Matlab/Octave on realistic data will accompany the lectures.


Nocedal, Jorge, and Stephen Wright. Numerical optimization. Springer Science & Business Media, 2006.

Beck, Amir. First-order methods in optimization. Society for Industrial and Applied Mathematics, 2017.

Gillis, Nicolas. Nonnegative Matrix Factorization. SIAM, 2020.

Dumitrescu, Bogdan, and Paul Irofti. Dictionary learning algorithms and applications. Springer, 2018.

Other textbooks, recent scientific articles and case studies from real world applications will be made available during the course.

Metodi didattici

Blackboard, slides and computer lab sessions.

Modalità di verifica e valutazione dell'apprendimento

Oral presentation of a takehome project (with slides)

Strumenti a supporto della didattica

Slides made available as pdf file on the course webpage. Use of Matlab computational environment, and various toolboxes.


see also:   http://

Orario di ricevimento

Consulta il sito web di Valeria Simoncini

Consulta il sito web di Margherita Porcelli