Unit NUMERICAL METHODS FOR DATA SCIENCE
- Course
- Mathematics
- Study-unit Code
- A005437
- Curriculum
- Matematica per analisi di sistemi complessi e dati
- Teachers
-
- Bruno Iannazzo (Codocenza)
- Hours
- 47 ore (Codocenza) - Bruno Iannazzo
- CFU
- 6
- Course Regulation
- Coorte 2025
- Offered
- 2025/26
- Learning activities
- Affine/integrativa
- Area
- Attività formative affini o integrative
- Academic discipline
- MAT/08
- Type of study-unit
- Opzionale (Optional)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- English (or Italian if all students agree).
- Contents
- Selected topics in Numerical Analysis related to approximation, linear algebra and non-linear optimization and their applications to data science.
- Reference texts
- For the numerical analysis topics: J. Stoer, R. Bulirsch. Introduction to numerical analysis. Springer. 2013; material provided by the teacher. For the optional topics, we will use a specific reference, that will be communicated during the lectures.
- Educational objectives
- Get into mathematical aspects related to some important data science and information technologies applications. Getting in touch with all mathematical modeling aspects: abstraction, modeling interpretation, computer simulation.
- Prerequisites
- Teaching methods
- Face-to-face lectures and laboratory sessions, using Matlab/Octave
- Other information
- Learning verification modality
- Oral exam and seminar. The oral exam can be substituted by a project and/or a seminar, upon availability. For information on support services for students with disabilities and/or learning disorders, please visit the page http://www.unipg.it/disabilita-e-dsa
- Extended program
- Least square approximation (linear fitting) and singular value decomposition (principal component analysis). QR factorization and Nonnegative matrix factorization. Problems with large datasets: Krylov, methods, Fast Fourier Trasnform. Non-linear and manifold optimization. During the course, some applications to data science and information technologies will be taken into account: data fitting, classification problems; machine learning.