Artificial intelligence and machine learning seminar

linfo2369  2022-2023  Louvain-la-Neuve

Artificial intelligence and machine learning seminar
3.00 credits
30.0 h
Q1
Teacher(s)
Jodogne Sébastien; Nijssen Siegfried;
Language
English
> French-friendly
Main themes
The topics covered in the seminar will address artificial intelligence and machine learning. In particular, scientific articles are selected in these fields.
On the one hand, students are confronted with problem of the quality of a scientific bibliography. Moreover, students read scientific literature (eg articles from international journals).
Learning outcomes

At the end of this learning unit, the student is able to :

1 Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
  • INFO1.1-3
  • INFO3.1, INFO3.2
  • INFO5.3-4, INFO5.6
  • INFO6.1, 6.4
Given the learning outcomes of the "Master [120] in Computer Science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
  • SINF1.M4
  • SINF3.1, SINF3.2
  • SINF5.3-4, SINF5.6
  • SINF6.1, SINF6.3, SINF6.4
Student completing successfully this course will be able to
  • establish the state of the art based on the scientific literature, when confronted with a research problem beyond his current knowledge,
  • prepare a comprehensive report including a scientific bibliography and explaining its relevance to a theme,
  • synthesize a scientific article by explaining the context, challenges, innovative results, potential applications as well as tracks for further work in the field,
  • communicate orally the results of a research to a public of computer scientists not experts in the field,
  • interact with a person who presents research results showing a critical and constructive look over the work presented.
 
Content
This seminar focuses on recent advances in artificial intelligence and machine learning.
Teaching methods
After a general introduction by the teacher, the seminar essentially consists of presentations made by the students. These presentations will consist of videos; other students are expected to watch these videos and ask questions about them.
Intermediary results are due before the final presentations (by default, given by groups of several students), including intermediate report(s) and submission to the teacher of the talk that will be presented.
A feedback about these intermediary results is given to each group, either directly or through the Moodle site.
Evaluation methods
The evaluation focuses on the quality of the presentations made by each student in front of the other participants to the seminar.
The overall grade consists of:
  • 80% for the quality of the presentation (teaching quality, correctness of technical content, references, ...)
  • 20% of the pro-activity of each student  (questions, additional comments, ...)
For the second session, the evaluation is based for 80% on a written report to the teacher the first day of the examination session + 20% for the participation grade during the year (grade fixed during the first session).
Other information
This seminar has as prerequisite de course LINGI2262 (Machine Learning :classification and evaluation) or the course LELEC2870 (Machine learning : regression, deep networks and dimensionality reduction).
Online resources
https://moodle.uclouvain.be/course/view.php?id=717
Bibliography
Des ouvrages ou articles recommandés sont mentionnés sur le site Moodle du cours.
Recommended textbooks or scientific papers are mentioned on the Moodle site for this course.
Faculty or entity
INFO


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Computer Science and Engineering
INFO2M
3
Master [120] in Computer Science
SINF2M
3
Master [120] in Data Science Engineering
DATE2M
3
Master [120] in Data Science: Information Technology
DATI2M
3