Artificial intelligence and machine learning seminar

LINGI2369  2018-2019  Louvain-la-Neuve

Artificial intelligence and machine learning seminar
3.0 credits
30.0 h
1q

Teacher(s)
Dupont Pierre ; Nijssen Siegfried ; Deville Yves (compensates Dupont Pierre) ; Schaus Pierre (compensates Dupont Pierre) ;
Language
Anglais
Main themes

The topics covered in the seminar will address artificial intelligence and machine learning. In particular, scientific articles are selected in these fields.

 

Aims

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”.

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 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

The research seminar should be followed the same year as the 'end of study work' because it is a methodological support to its realization.

It is not *mandatory but preferable* to select the option corresponding to the seminar in order to participate.

 

Faculty or entity


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

Program title
Sigle
Credits
Prerequisites
Aims
Master [120] in Computer Science and Engineering
3
-

Master [120] in Computer Science
3
-

Master [120] in Data Science Engineering
3
-

Master [120] in data Science: Information technology
3
-