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Study programme 2015-2016

Teaching and training




Students who choose to major in artificial intelligence must be able to:
  • Identify and use methods and techniques that create software-based solutions to complex problems,
  • Understand and put to good use the methods and techniques pertaining to artificial intelligence such as automated reasoning, heuristic research, knowledge acquisition, automated learning, problems related to constraint satisfaction,
  • Identify a category of applications and how to use its methods and tools; understand specific categories of applications and their specific techniques-for example computer vision, scheduling, data mining, natural language processing, bioinformatics data,
  • Formalise and structure a body of complex knowledge by using a systematic and rigorous approach to develop quality “intelligent” systems.

 
> Legend

The student may select

De 20 à 30 credits parmi
Annual block
  1 2

Mandatory Required courses in Artificial Intelligence
Mandatory LINGI2262 Machine Learning :classification and evaluation   Pierre Dupont 30h+30h  5 credits 2q x x
Mandatory LINGI2263 Computational Linguistics   Pierre Dupont, Cédrick Fairon 30h+15h  5 credits 1q x x
Mandatory LINGI2266 Advanced Algorithms for Optimization   Pierre Schaus 30h+15h  5 credits 1q x x
Mandatory LINGI2365 Constraint programming   Yves Deville, Jean-Baptiste Mairy (compensates Yves Deville) 30h+15h  5 credits 2q x x
 
Optionnal Elective courses in Artificial Itelligence

The student can select 10 credits between:  

Optionnal LSINF2275 Data mining & decision making   Marco Saerens 30h+30h  5 credits 2q x x
Optionnal LELEC2885 Image processing and computer vision   Christophe De Vleeschouwer, Laurent Jacques 30h+30h  5 credits 1q x x
Optionnal LGBIO2010 Bioinformatics   Pierre Dupont, Michel Ghislain 30h+30h  5 credits 2q x x
Optionnal LINMA1702 Applied mathematics : Optimization I   François Glineur 30h+22.5h  5 credits 2q x x
Optionnal LINMA1691 Discrete mathematics - Graph theory and algorithms   Vincent Blondel, Jean-Charles Delvenne (compensates Vincent Blondel) 30h+22.5h  5 credits 1q x x
Optionnal LINMA2111 Discrete mathematics II : Algorithms and complexity   Vincent Blondel, Jean-Charles Delvenne (coord.) 30h+22.5h  5 credits 2q x x
Optionnal LSTAT2320 Design of experiment.   Patrick Bogaert, Bernadette Govaerts 22.5h+7.5h  5 credits 2q x x
Optionnal LELEC2870 Machine Learning : regression, dimensionality reduction and data visualization   John Lee (compensates Michel Verleysen), Michel Verleysen 30h+30h  5 credits 1q x x
Optionnal LINMA2450 Combinatorial optimization   Jean-Charles Delvenne, Julien Hendrickx 30h+22.5h  5 credits 1q x x