Major in Artificial Intelligence: big data, optimization and algorithms

info2m  2019-2020  Louvain-la-Neuve

Students completing the major in artificial intelligence: big data, optimization and algorithms will 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, big data processing; Formalise and structure a body of complex knowledge by using a systematic and rigorous approach to develop quality “intelligent” systems.


 
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Students shall select 20 to 30 credits among

Annual block
  1 2

Mandatory Contenu:
Mandatory Required courses in Artificial Intelligence: big data, optimization and algortihms
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
, Pierre Schaus
, Pierre Schaus (compensates Yves Deville)
30h+15h  5 credits 2q x x
Optionnal Elective courses in Artificial Itelligence

Student shall select 10 credits among  

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 LELEC2885 Image processing and computer vision   Christophe De Vleeschouwer (coord.)
, Laurent Jacques
30h+30h  5 credits 1q x x
Optionnal LGBIO2010 Bioinformatics   Pierre Dupont
30h+30h  5 credits 1q x x
Optionnal LINGI2145 Cloud Computing   Etienne Riviere
30h+15h  5 credits 1q x x
Optionnal LINGI2364 Mining Patterns in Data   Siegfried Nijssen
30h+15h  5 credits 2q x x
Optionnal LINMA1691 Discrete mathematics - Graph theory and algorithms   Vincent Blondel
, Jean-Charles Delvenne
30h+22.5h  5 credits 1q x x
Optionnal LINMA1702 Optimization models and methods I   François Glineur
30h+22.5h  5 credits 2q x x
Optionnal LINMA2450 Combinatorial optimization   Jean-Charles Delvenne (coord.)
, Julien Hendrickx
30h+22.5h  5 credits 1q x x
Optionnal LINMA2472 Algorithms in data science   Vincent Blondel
, Jean-Charles Delvenne (coord.)
, Gautier Krings (compensates Vincent Blondel)
30h+22.5h  5 credits 1q x x
Optionnal LSINF2275 Data mining & decision making   Marco Saerens
30h+15h  5 credits 2q x x