Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
3 credits
30.0 h
Q1
Teacher(s)
Dupont Pierre; Nijssen Siegfried;
Language
English
Prerequisites
The research seminar should ideally be followed the same year as the master thesis because it is a methodological support to its realization.
It is not *mandatory but preferable* to select the seminar matching the principal option(s) chosen by a student.
Having followed LINGI2261
It is not *mandatory but preferable* to select the seminar matching the principal option(s) chosen by a student.
Having followed LINGI2261
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).
Aims
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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The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
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 several talks given by the students.
Intermediary results are due before the final talks (by default, given by groups of several students), including intermediate report(s) and submission to the teacher of the slides 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 talks (by default, given by groups of several students), including intermediate report(s) and submission to the teacher of the slides 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:
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 when attending other presentations (questions, additional comments, ...)
Other information
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
