3.00 credits
30.0 h
Q1
Teacher(s)
Jodogne Sébastien; Nijssen Siegfried;
Language
English
> French-friendly
> 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).
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:
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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 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.
Intermediary results are due before the final presentations (by default, given by groups of several students), including intermediate report(s) and submission 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:
Failure to comply with the methodological instructions communicated by the teacher, particularly with regard to the use of online resources or collaboration between students, will result in an overall mark of 0. The use of generative AI tools without prior permission is strictly prohibited.
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...)
Failure to comply with the methodological instructions communicated by the teacher, particularly with regard to the use of online resources or collaboration between students, will result in an overall mark of 0. The use of generative AI tools without prior permission is strictly prohibited.
Other information
This seminar has as prerequisite de course LINFO2262 (Machine Learning: classification and evaluation) or the course LELEC2870 (Machine learning: regression, deep networks and dimensionality reduction).
Online resources
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.
Recommended textbooks or scientific papers are mentioned on the Moodle site for this course.
Faculty or entity
INFO
