Unit NUMERICAL METHODS FOR INFORMATION TECHNOLOGIES
- Course
- Informatics
- Study-unit Code
- A002096
- Curriculum
- Cybersecurity
- Teachers
-
- Bruno Iannazzo (Codocenza)
- Hours
- 47 ore (Codocenza) - Bruno Iannazzo
- CFU
- 6
- Course Regulation
- Coorte 2025
- Offered
- 2026/27
- Learning activities
- Affine/integrativa
- Area
- Attività formative affini o integrative
- Sector
- MAT/08
- Type of study-unit
- Opzionale (Optional)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- English (or Italian upon request).
- Contents
- Numerical aspects of approximation, linear algebra and optimization with application to Data Science and Information Technologies.
- Reference texts
- Slides of the course will be provided by the teacher. The textbook is: J. Stoer, R. Bulirsh. Introduction to numerical analysis. Springer. 2013.
For optional arguments the reference will be communicated during the course. - Educational objectives
- Get into the mathematical aspects of some important applications to Data Science and Information Technologies. Ability to work out all the aspects of the mathematical modelling, from abstract mathematics to its model interpretation and the computer simulation.
- Prerequisites
- Linear algebra. Differential calculus of function or real variables.
- Teaching methods
- Lectures and MATLAB laboratory.
- Learning verification modality
- Oral exam. The oral exam can be substituted with a mid term seminar and a final project.
The objective of the oral exam is to examine how the student manage the topics of the course.
The objective of the mid term is to examine the ability to present a topic related to the course but studied autonomously. - Extended program
- Least squares approximation and singular value decomposition (principal component analysis). QR factorization and nonnegative matrix factorization. Trigonometric interpolation and Fast Fourier Transform. Nonlinear optimization and manifold optimization. Applications: data fitting; classification problems; machine learning.