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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.
Courses not taught this academic yearPeriodic courses not taught this academic year
Periodic courses taught this academic yearTwo year courses

Click on the course code to see detailed informations (objectives, methods, evaluation...)

The student shall select 30 credits from amongst

MandatoryCompulsory courses in Artifficial intelligence
Mandatory LINGI2262

Machine Learning :classification and evaluation Pierre Dupont30h + 30h 5credits 1q xx
Mandatory LINGI2263

Computational Linguistics Pierre Dupont (coord.), Cédrick Fairon30h + 15h 5credits 2q xx
Mandatory LINGI2264

Automated reasoning Charles Pecheur30h + 15h 5credits 1q xx
Mandatory LINGI2365

Constraint programming Yves Deville30h + 15h 5credits 2q xx

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

Data mining & decision making Marco Saerens30h + 30h 5credits 2q xx
Optional LELEC2885

Image processing and computer vision Christophe De Vleeschouwer (coord.), Laurent Jacques (supplée Benoît Macq), Benoît Macq30h + 30h 5credits 1q xx
Optional LINGI2368

Computational biology Pierre Dupont30h + 15h 5credits 1q xx
Optional LGBIO2010

Bioinformatics Yves Deville, Michel Ghislain30h + 30h 5credits 2q xx
Optional LINMA1702

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

Discrete mathematics - Graph theory and algorithms  Vincent Blondel30h + 22.5h 5credits 1q xx
Optional LINMA2111

Discrete mathematics II : Algorithms and complexity  Vincent Blondel30h + 22.5h 5credits 2q xx
Optional LSTAT2110

Data Analysis Christian Hafner, Cédric Heuchenne (supplée Christian Hafner), Johan Segers22.5h + 7.5h 5credits 1q xx
Optional LSTAT2320

Design of experiment. Patrick Bogaert, Bernadette Govaerts22.5h + 7.5h 5credits 2q xx
Optional LSTAT2020

Statistical computing Céline Bugli (supplée Bernadette Govaerts), Bernadette Govaerts20h + 20h 6credits 1q xx
Optional LELEC2870

Machine Learning : regression, dimensionality reduction and data visualization Michel Verleysen30h + 30h 5credits 1q xx
Optional LINGE1222

Multivariate Statistical Analysis Johan Segers30h + 15h 4credits 2q xx
Optional LINMA2450

Combinatorial optimization  Jean-Charles Delvenne30h + 22.5h 5credits 1q xx
| 23/11/2010 |