Major in Artificial Intelligence: big data, optimization and algorithms

Students completing the major in Artificial Intelligence: big data, optimization and algorithms will 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, big data processing,
  • Formalise and structure a body of complex knowledge and use a systematic and rigorous approach to develop quality “intelligence” systems.

 
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Mandatory Optional
Course not taught in 2020-2021 Periodic course not taught in 2020-2021
Periodic course taught in 2020-2021 Activity with prerequisites
Click on the course title to see detailed informations (objectives, teaching methods, evaluation...)
The student shall select
From 20 to 30 credits
Annual unit
  1 2

Mandatory Content:
Mandatory Required courses in Artificial Intelligence: big data, optimization and algortihms
Mandatory LINGI2266 Advanced Algorithms for Optimization   Pierre Schaus
30h+15h  5 credits q1 x x
Mandatory LINGI2263 Computational Linguistics   Pierre Dupont
, Pierre Dupont (compensates Cédrick Fairon)
,
30h+15h  5 credits q1 x x
Mandatory LINGI2364 Mining Patterns in Data   Siegfried Nijssen
30h+15h  5 credits q2 x x
Mandatory LINGI2365 Constraint programming   Pierre Schaus
, Pierre Schaus (compensates Yves Deville)
30h+15h  5 credits q2 x x
Optional Elective courses in Artificial Itelligence
The student select 10 credits among
Optional LELEC2870 Machine learning : regression, deep networks and dimensionality reduction   John Lee
, Michel Verleysen
30h+30h  5 credits q1 x x
Optional LELEC2885 Image processing and computer vision   Christophe De Vleeschouwer (coord.)
, Laurent Jacques
30h+30h  5 credits q1 x x
Optional LGBIO2010 Bioinformatics   Pierre Dupont
30h+30h  5 credits q1 x x
Optional LINGI2145 Cloud Computing   Etienne Riviere
30h+15h  5 credits q1 x x
Optional LINMA1691 Discrete mathematics - Graph theory and algorithms   Vincent Blondel
, Jean-Charles Delvenne
30h+22.5h  5 credits q1 x x
Optional LINMA1702 Optimization models and methods I   François Glineur
30h+22.5h  5 credits q2 x x
Optional LINMA2450 Combinatorial optimization   Jean-Charles Delvenne
, Julien Hendrickx
30h+22.5h  5 credits q1 x x
Optional LINMA2472 Algorithms in data science   Jean-Charles Delvenne (coord.)
, Gautier Krings (compensates Vincent Blondel)
30h+22.5h  5 credits q1 x x
Optional LSINF2275 Data mining & decision making   Marco Saerens
30h+15h  5 credits q2 x x