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Study programme 2015-2016

Teaching and training




Students choosing to major in Artificial Intelligence must be able to:
  • Identify and implement methods and techniques that allow software to solve complex problems that when solved by humans require “intelligence”,
  • Understand and put to good use methods and techniques relating to artificial intelligence such as automatic reasoning, research and heuristics, acquisition and representation of knowledge, automatic learning, problems associated with overcoming constraints,
  • Identify applications and its methods and tools; understand a particular category of applications and its related techniques, for example robotics, computer vision, planning, data mining, computational linguistics and bioinformatics,
  • Formalise and structure a body of complex knowledge and use a systematic and rigorous approach to develop quality “intelligence” systems.

 
> Legend

The student shall 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