Students completing successfully this option will be able to
• identify and implement a class of methods and techniques to design software able to solve complex problems which, if solved by a human beings, would require "intelligence" ;
• select and apply wisely methods and techniques related to artificial intelligence such as automated reasoning, heuristic search, acquisition and knowledge representation, machine learning, and constraint satisfaction problems ;
• identify types of applications where these methods and tools can be applied; master some of these applications and their specific techniques - for example, robotics, computer vision, planning, data mining, natural language processing and bioinformatics ;
• formalize and structure body of complex knowledge using a systematic and rigorous approach to develop quality "intelligent" systems.
• identify and implement a class of methods and techniques to design software able to solve complex problems which, if solved by a human beings, would require "intelligence" ;
• select and apply wisely methods and techniques related to artificial intelligence such as automated reasoning, heuristic search, acquisition and knowledge representation, machine learning, and constraint satisfaction problems ;
• identify types of applications where these methods and tools can be applied; master some of these applications and their specific techniques - for example, robotics, computer vision, planning, data mining, natural language processing and bioinformatics ;
• formalize and structure body of complex knowledge using a systematic and rigorous approach to develop quality "intelligent" systems.
> Legend | ||||||||
The student shall select |
||||||||
De 20 à 30 credits parmi | ||||||||
Year | ||||||||
1 | 2 | |||||||
Compulsory courses in Artifficial intelligence | ||||||||
LINGI2262 | Machine Learning :classification and evaluation | Pierre Dupont | 30h+30h | 5 credits | 2q | x | x | |
LINGI2263 | Computational Linguistics | Pierre Dupont, Cédrick Fairon | 30h+15h | 5 credits | 1q | x | x | |
LINGI2266 | Advanced Algorithms for Optimization | Pierre Schaus | 30h+15h | 5 credits | 1q | x | x | |
LINGI2365 | Constraint programming | Yves Deville | 30h+15h | 5 credits | 2q | x | x | |
Elective courses in Artificial Itelligence
The student can select 10 credits amongst |
||||||||
LSINF2275 | Data mining & decision making | Marco Saerens | 30h+30h | 5 credits | 2q | x | x | |
LELEC2885 | Image processing and computer vision | Christophe De Vleeschouwer (coord.), Laurent Jacques (compensates Christophe De Vleeschouwer), Benoît Macq | 30h+30h | 5 credits | 1q | x | x | |
LGBIO2010 | Bioinformatics | Pierre Dupont, Michel Ghislain | 30h+30h | 5 credits | 2q | x | x | |
LINMA1702 | Applied mathematics : Optimization I | Vincent Blondel, François Glineur (compensates Vincent Blondel), François Glineur (coord.) | 30h+22.5h | 5 credits | 2q | x | x | |
LINMA1691 | Discrete mathematics - Graph theory and algorithms | Vincent Blondel, Jean-Charles Delvenne (compensates Vincent Blondel) | 30h+22.5h | 5 credits | 1q | x | x | |
LINMA2111 | Discrete mathematics II : Algorithms and complexity | Vincent Blondel, Jean-Charles Delvenne (compensates Vincent Blondel) | 30h+22.5h | 5 credits | 2q | x | x | |
LSTAT2320 | Design of experiment. | Patrick Bogaert, Bernadette Govaerts | 22.5h+7.5h | 5 credits | 2q | x | x | |
LELEC2870 | Machine Learning : regression, dimensionality reduction and data visualization | John Lee (compensates Michel Verleysen), Michel Verleysen | 30h+30h | 5 credits | 1q | x | x | |
LINMA2450 | Combinatorial optimization | Jean-Charles Delvenne | 30h+22.5h | 5 credits | 1q | x | x | |
LINGI2264 | Automated reasoning | N. | 30h+15h | 5 credits | 1q | x | x |