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Artificial Intelligence [30.0]

Students who have completed the "Artificial Intelligence" option will have to be able to:
  • Identify and implement a class of methods and techniques enabling a software system to solve complex problems which, when solved by a human being, would require some form of "intelligence".
  • Understand and effectively implement methods and techniques of artificial intelligence such as automated reasoning, search and heuristics, knowledge acquisition and representation, automated learning, constraint satisfaction problems.
  • Identify those classes of applications where these methods and tools can be applied; be aware of particular classes of application and their specific techniques – for example, robotics, computer vision, planning, data mining, natural language processing and bioinformatics data processing.
  • Formalize and structure complex bodies of knowledge by using a systematic and rigorous approach to develop "intelligent" systems of high quality.
Legend
Mandatory Optional
Courses not taught this academic year Periodic courses not taught this academic year
Periodic courses taught this academic year Two year courses
Click on the course code to see detailed informations (objectives, methods, evaluation...)
Year
1 2

The student shall select 30 credits from amongst

MandatoryCompulsory courses in Artifficial intelligence
Mandatory LINGI2262

 Machine Learning :classification and evaluation  Pierre Dupont 30h + 30h  5credits  1q  x x
Mandatory LINGI2263

 Computational Linguistics  Pierre Dupont (coord.), Cédrick Fairon 30h + 15h  5credits  2q  x x
Mandatory LINGI2264

 Automated reasoning  Charles Pecheur 30h + 15h  5credits  1q  x x
Mandatory LINGI2365

 Constraint programming  Yves Deville 30h + 15h  5credits  2q  x x

MandatoryElective courses in Artificial Itelligence
The student shall select 10 credits from amongst
Optional LSINF2275

 Data mining & decision making  Marco Saerens 30h + 30h  5credits  2q  x x
Optional LELEC2885

 Image processing and computer vision  Christophe De Vleeschouwer (coord.), Laurent Jacques (supplée Benoît Macq), Benoît Macq 30h + 30h  5credits  1q  x x
Optional LINGI2368

 Computational biology  Pierre Dupont 30h + 15h  5credits  1q  x x
Optional LGBIO2010

 Bioinformatics  Yves Deville, Michel Ghislain 30h + 30h  5credits  2q  x x
Optional LINMA1702

 Applied mathematics : Optimization  Vincent Blondel, François Glineur (coord.) 30h + 22.5h  5credits  2q  x x
Optional LINMA1691

 Discrete mathematics - Graph theory and algorithms   Vincent Blondel 30h + 22.5h  5credits  1q  x x
Optional LINMA2111

 Discrete mathematics II : Algorithms and complexity   Vincent Blondel 30h + 22.5h  5credits  2q  x x
Optional LSTAT2110

 Data Analysis  Christian Hafner, Johan Segers 22.5h + 7.5h  5credits  1q  x x
Optional LSTAT2320

 Design of experiment.  Patrick Bogaert, Bernadette Govaerts 22.5h + 7.5h  5credits  2q  x x
Optional LSTAT2020

 Statistical computing  Bernadette Govaerts, Christian Ritter (supplée Bernadette Govaerts) 20h + 20h  6credits  1q  x x
Optional LELEC2870

 Machine Learning : regression, dimensionality reduction and data visualization  Michel Verleysen 30h + 30h  5credits  1q  x x
Optional LINGE1222

 Multivariate Statistical Analysis  (in French) Johan Segers 30h + 15h  4credits  2q  x x
Optional LINMA2450

 Combinatorial optimization   Jean-Charles Delvenne 30h + 22.5h  5credits  1q  x x
 
| 23/11/2010 |