Duration
24h Th, 14h Pr, 6h Labo., 45h Proj.
Number of credits
| Bachelor of Science (BSc) in Computer Science | 5 crédits | |||
| Master of Science (MSc) in Computer Science | 5 crédits |
Lecturer
Language(s) of instruction
French language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Numerical analysis is at the boundary between Mathematics and Computer Science. It consists in studying how to practically obtain in a computer different mathematical concepts studied in other courses.
This course gives a brief introduction on good ways to implement a numerical method.
The following topics are considered:
- number representations in a computer
- Non-linear equations and systems
- interpolation and linear regression
- Linear algebra
- Sparse linear algebra
- Monte-Carlo methods
Learning outcomes of the learning unit
- representation of numbers in a computer and implications on the roundoff errors in floating point computing
- interpoolation issues
- numerical methods for the resolution of nonlinear equations
- basics of numerical linear algebra
- basics of sparse linear algebra
Prerequisite knowledge and skills
A basic course in linear algebra
Planned learning activities and teaching methods
Five tutorials are organized.
A collaborative online international learning (COIL) is organized with a university in Brazil. In this context, the students need to collaborate with the brazilian students in order to provide a working numerical code in python.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Additional information:
face-to-face
Recommended or required readings
Lecture notes are available on the course's website.
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions )
Written work / report
Additional information:
A written exam is organized and counts for 2/3 of the final grade.
The implementation project in collaboration with Brazil counts for 1/3 of the final grade. It must be submitted in October. The collaborative project cannot be resubmitted in second session.
If the collaborative project is not submitted in October, an individual implementation project in C must be submitted.
Work placement(s)
Organizational remarks
All documents are available through a OneDrive repository.
Contacts
q.louveaux@uliege.be
dlamy@uliege.be for the exercises