Study Programmes 2017-2018
WARNING : 2016-2017 version of the course specifications
Introduction to machine learning
Duration :
30h Th, 5h Pr, 40h Proj.
Number of credits :
Master in biomedical engineering (120 ECTS)5
Master in data science (120 ECTS)5
Master in electrical engineering (120 ECTS)5
Master of science in computer science and engineering (120 ECTS)5
Master in data science and engineering (120 ECTS)5
Master in computer science (120 ECTS)5
Master in bio-informatics and modelling (120 ECTS)6
Master in mathematics (120 ECTS)8
Lecturer :
Pierre Geurts, Louis Wehenkel
Coordinator :
Language(s) of instruction :
English language
Organisation and examination :
Teaching in the first semester, review in January
Units courses prerequisite and corequisite :
Prerequisite or corequisite units are presented within each program
Learning unit 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.
Learning outcomes of the learning unit :
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
Prerequisite knowledge and skills :
Elements of probability calculus, statistics, algorithmics, and optimlization
Planned learning activities and teaching methods :
Theoretical ex cathedra course combined with personal howeworks using the computer
Mode of delivery (face-to-face ; distance-learning) :
1st semester
Recommended or required readings :
Assessment methods and criteria :
Practical work (competition among students to solve a particular problem), and oral exam about the theoretical assimilation of the course material
Work placement(s) :
Organizational remarks :
Web page:
Contacts : )