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| ELEN0062-1 | Applied Inductive Learning
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| Duration : | 30h Th, 5h Pr, 40h Proj. |
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| Number of credits : |
| Master in Biomedical Engineering, research focus, 2nd year | | 5 |
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| Master in Biomedical Engineering, research focus, 2nd year | | 5 |
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| Master in Electrical Engineering, research focus, 2nd year | | 5 |
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| Master in Electrical Engineering, research focus, 2nd year | | 5 |
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| Master of science in computer science and engineering, research focus, 1st year | | 5 |
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| Master of science in computer science and engineering, research focus, 2nd year | | 5 |
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| Master in Computer science, Research Focus, 1st year | | 5 |
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| Master in Computer science, Research Focus, 2nd year | | 5 |
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| Master of science in computer science and engineering, professional focus in management, 1st year | | 5 |
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| Master in Computer Science, Professional Focus (Management), 1st year | | 5 |
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| Master in Bio-informatics and Modelling, Research focus, 1st year | | 6 |
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| Master in Bio-informatics and Modelling, Research focus, 2nd year | | 6 |
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| Master en sciences mathématiques, à finalité spécialisée en statistique, 2nd year | | 8 |
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| Lecturer : | Pierre Geurts, Louis Wehenkel |
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| Coordinator : | N... |
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Language(s) of instruction :
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| English language |
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Organisation and examination :
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| Teaching in the first semester, review in January |
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Course contents :
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| 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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Learning outcomes of the course :
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| The student should be able to analyze the theoretical (computational and statistical) properties of the most important machine learning algorithms, to apply them in practice, and to assess in a sound way their performances |
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Prerequisites and co-requisites/ Recommended optional programme components :
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| Elements of probability calculus, statistics, algorithmics, and optimlization |
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Planned learning activities and teaching methods :
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| Theoretical ex cathedra course combined with personal howeworks using the computer |
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Mode of delivery (face-to-face ; distance-learning) :
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| 1st semester |
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Recommended or required readings :
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Assessment methods and criteria :
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| Practical work (competition among students to solve a particular problem), and oral exam about the theoretical assimilation of the course material |
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Work placement(s) :
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Organizational remarks :
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| Web page: http://www.montefiore.ulg.ac.be/~lwh/AIA |
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Contacts :
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| mailto:L.Wehenkel@ulg.ac.be(L.Wehenkel@ulg.ac.be
)mailto:p.geurts@ulg.ac.be(p.geurts@ulg.ac.be) |
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