Master Thesis Online
Master thesis : Optimization Techniques for AutoML systems
Vieslet, Thomas 
Promotor(s) :
Geurts, Pierre
Date of defense : 28-Jan-2022 • Permalink : http://hdl.handle.net/2268.2/13920
Details
| Title : | Master thesis : Optimization Techniques for AutoML systems |
| Translated title : | [fr] Techniques d'optimisation pour des systèmes AutoML |
| Author : | Vieslet, Thomas
|
| Date of defense : | 28-Jan-2022 |
| Advisor(s) : | Geurts, Pierre
|
| Committee's member(s) : | Louppe, Gilles
Louveaux, Quentin Lahouli, Ichraf |
| Language : | English |
| Number of pages : | 72 |
| Keywords : | [en] AutoML, Optimization Techniques |
| Discipline(s) : | Engineering, computing & technology > Computer science |
| Target public : | Researchers Professionals of domain Student General public |
| Institution(s) : | Université de Liège, Liège, Belgique |
| Degree: | Master : ingénieur civil en science des données, à finalité spécialisée |
| Faculty: | Master thesis of the Faculté des Sciences appliquées |
Abstract
[en] In this Master Thesis is investigated several optimization techniques in the context of automatizing the Machine Learning Pipeline. The goal is to use these techniques to find the best set of features together with best model with the best hyperparameters values. The performance of the techniques are compared on various tasks.
File(s)
Document(s)
tfe.pdf
Description: The link to the code is on page 3
Size: 6.03 MB
Format: Adobe PDF
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The University of Liège does not guarantee the scientific quality of these students' works or the accuracy of all the information they contain.