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| ELEN0062-1 | Applied Inductive Learning
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| Duration : | 30h Th, 30h Pr |
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| Credits/ECTS : |
| civil engineering in electricity, 3rd year | | Premier quadrimestre | | 6,5 |
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| civil engineer in computer sciences, 3rd year | | Premier quadrimestre | | 5,5 |
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| Master in Biomedical Engineering, in-depth approach, 2nd year | | Premier quadrimestre | | 5 |
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| Master in Electrical Engineering, in-depth approach, 2nd year | | Premier quadrimestre | | 5 |
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| Master in Computer Engineering, in-depth approach, 2nd year | | Premier quadrimestre | | 5 |
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| Master in Computer science, Research Focus, 2nd year | | Premier quadrimestre | | 6 |
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| Master in Bio-informatics and Modelling, Research focus, 1st year | | Premier quadrimestre | | 6 |
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| Master in Statistics : General, Research focus, 2nd year | | Premier quadrimestre | | 6 |
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| Master in Statistics : General, Professional focus, 2nd year | | Premier quadrimestre | | 6 |
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| Holder(s) : | Pierre Geurts, Louis Wehenkel |
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| Language : | Langue française |
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| Course contents : | Inductive learning consists of building automatically a general solution to a problem from a set of solutions of specific instances of this problem. Its applications are multitudinous: extraction medical diagnostic decision rules from clinical databases; bioinformatics; construction of credit allocation procedures from bank customer databases; computer vision; modeling, optimisation and control of complex systems; automatic syntesis of algorithms; extraction of knowledge from human experts... The theoretcal part of the course introduces the different types of automatic learning problems (explorative data mining, automatic classification automatique, approximation), the main underlying principles (bias/variance tradeoff, validation) as well as the main families of methods (statistical, symbolic, artificial neural nets). Practical exercises allow the students to become familiar with these concepts by applying them to a real databases. |
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| Organization : | 1st semester |
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| Remarks : | Web page: http://www.montefiore.ulg.ac.be/~lwh/AIA |
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