Artificial intelligence and machine learning seminar

lingi2369  2020-2021  Louvain-la-Neuve

Artificial intelligence and machine learning seminar
Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
3 credits
30.0 h
Q1
Language
English
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).
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:
  • 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.
 

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

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

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 talk that will be presented.
A feedback about these intermediary results is given to each group, either directly or through the Moodle site.
Given the sanitary situation, this seminar will be fully given online (distance learning) through Teams and the Moodle site.
Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

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 when attending other presentations (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
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


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

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Computer Science and Engineering

Master [120] in Computer Science

Master [120] in Data Science Engineering

Master [120] in Data Science: Information Technology