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teaching:mfe:ia [2017/04/20 18:29] stuetzle [Applications of a hybrid SLS algorithm framework] |
teaching:mfe:ia [2018/05/02 14:41] bersini |
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| - | ====== MFE 2015-2016 : Intelligence Artificielle ====== | + | ====== MFE 2017-2018 : Intelligence Artificielle ====== |
| ===== Introduction ===== | ===== Introduction ===== | ||
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| Ce mémoire se fera en collaboration avec l'équipe médicale du centre pour l'anorexie et la boulimie de l'hôpital Erasme. Il consistera en l'analyse informatisée des données récoltées lors d'entretiens avec le patient et sa famille au cours du traitement. Les données sont actuellement stockées dans dans une base de données SPSS. Le mémoire consistera pour l'essentiel au traitement de ces données par des approches "Machine Learning" et "Data Mining" dans une perspective de Quality Management. | Ce mémoire se fera en collaboration avec l'équipe médicale du centre pour l'anorexie et la boulimie de l'hôpital Erasme. Il consistera en l'analyse informatisée des données récoltées lors d'entretiens avec le patient et sa famille au cours du traitement. Les données sont actuellement stockées dans dans une base de données SPSS. Le mémoire consistera pour l'essentiel au traitement de ces données par des approches "Machine Learning" et "Data Mining" dans une perspective de Quality Management. | ||
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| + | * Contact : [[http://code.ulb.ac.be/iridia.people.php?id=1|Hugues Bersini (IRIDIA)]] | ||
| + | |||
| + | ===== Développer des solutions de text mining pour la société Procter & Gamble ===== | ||
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| + | Ce mémoire se fera en collaboration avec des responsables marketting de cette société. Il consistera en l'analyse informatisée des appréciations récoltées sur les produits de la firme et d'une analyse comparative avec les appréciations de concurrents. Il s'agira d'études de type "clustering", "classification" et "sentiment analysis". | ||
| * Contact : [[http://code.ulb.ac.be/iridia.people.php?id=1|Hugues Bersini (IRIDIA)]] | * Contact : [[http://code.ulb.ac.be/iridia.people.php?id=1|Hugues Bersini (IRIDIA)]] | ||
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| * Contact : [[http://code.ulb.ac.be/iridia.people.php?id=1|Hugues Bersini (IRIDIA)]] | * Contact : [[http://code.ulb.ac.be/iridia.people.php?id=1|Hugues Bersini (IRIDIA)]] | ||
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| - | ===== Etude pratique et expérimentale du langage de programmation F# ===== | ||
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| - | Depuis quelques années, Microsoft met en avant un nouveau langage de programmation F# créé dans le sillage des langages dits déclaratifs ou fonctionnels. Il semblerait que ce langage soit idéal pour le traitement des données. Le MFE consistera en une étude expérimentale de ce langage et un comparatif avec les langages de programmation aujourd'hui les plus usités. | ||
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| - | * Contact : [[http://code.ulb.ac.be/iridia.people.php?id=1|Hugues Bersini (IRIDIA)]] | ||
| ===== Etude de l'algorithme du Deep Learning ===== | ===== Etude de l'algorithme du Deep Learning ===== | ||
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| * Contact : [[http://code.ulb.ac.be/iridia.people.php?id=1|Hugues Bersini (IRIDIA)]] | * Contact : [[http://code.ulb.ac.be/iridia.people.php?id=1|Hugues Bersini (IRIDIA)]] | ||
| + | ===== Utilisation de réseaux de neurones récurrents pour la détection d'arythmies cardiaques ===== | ||
| + | Ce mémoire s'effectuera en collaboration avec le service de cardiologie d'Erasme et consistera en la détection automatique d'apparition d'arythmies cardiaques à l'aide des réseaux de neurones récurrents, qui semblent être très efficaces pour le traitement des séries chronologiques complexes. | ||
| - | ===== Software framework for Ant Colony Optimization ===== | + | * Contact : [[http://code.ulb.ac.be/iridia.people.php?id=1|Hugues Bersini (IRIDIA)]] |
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| + | ===== Text Categorisation and quality control through automatic language processing ===== | ||
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| + | This thesis is developed in collaboration with the Energy Efficiency in Industrial Processes (EEIP) company. EEIP is a global industry information network. As part of their activities, they disseminate case studies to various network groups. The goal of the project is to develop an automatic language processing algorithm capable to evaluate the quality (accept / reject) of the proposed case studies and to allocate them to single/multiple categories. Testing and training the algorithm is a key part as it not only requires development and testing of concepts such as how to evaluate quality or definition of requirements for multiple category allocation but the project also has | ||
| + | to be developed in a limited data environment (+/- 1000 case studies as training set). | ||
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| + | Required skills: A background in machine learning would be helpful. | ||
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| + | |||
| + | * Contacts : | ||
| + | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
| + | * [[https://www.ee-ip.org/|Jürgen Ritzek (EE-IP)]] | ||
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| + | ===== Software framework for ant colony optimization ===== | ||
| Ants have inspired a number of computational techniques and among the most successful is ant colony optimization (ACO). ACO is an optimization technique that can be applied to tackle a wide variety of computational problems that arise in computer science, telecommunications, and engineering. While ACO has a very wide applicability, the development times for effective ACO algorithms can be relatively high. This is due to the fact that each time a new problem is to be tackled by an ACO algorithm, a researcher needs to implement the algorithms almost from scratch. | Ants have inspired a number of computational techniques and among the most successful is ant colony optimization (ACO). ACO is an optimization technique that can be applied to tackle a wide variety of computational problems that arise in computer science, telecommunications, and engineering. While ACO has a very wide applicability, the development times for effective ACO algorithms can be relatively high. This is due to the fact that each time a new problem is to be tackled by an ACO algorithm, a researcher needs to implement the algorithms almost from scratch. | ||
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| The application of this software framework will be tested on a number of optimization problems. | The application of this software framework will be tested on a number of optimization problems. | ||
| - | Required skills: The candidate should be well acquainted with programming in object oriented languages. | + | Required skills: The candidate should be well acquainted with programming in object oriented languages. |
| * Contacts : | * Contacts : | ||
| * [[http://iridia.ulb.ac.be/~mdorigo|Marco Dorigo (IRIDIA)]] | * [[http://iridia.ulb.ac.be/~mdorigo|Marco Dorigo (IRIDIA)]] | ||
| + | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
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| + | ===== Automated configuration of multi-objective continuous optimizers ===== | ||
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| + | Many problems arising in real-world applications involve the optimization of various, often conflicting objectives. While the design of algorithms for tackling multi-objective problems has usually done manually, over the recent years automated design methodologies have been established and proved to be very powerful. | ||
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| + | The goal of this project is to extend the automated design to multi-objective continuous optimization problems. As the basis of the approach, a framework based on the two-phase plus Pareto local search approach will be developed into which basic search techniques for continuous optimization will be integrated. The goal is to build first a flexible framework from which then in a second step effective multi-objective optimizers will be generated exploiting automated algorithm design techniques. The final goal of this work is to participate in algorithm competitions with the goal of challenging the methodology. | ||
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| + | Required skills: The candidate should have very good analytical as well as programming skills. | ||
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| + | * Contacts : | ||
| * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
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| - | ===== Automatic configuration of hybrid algorithms ===== | + | ===== Automated configuration of hybrid algorithms ===== |
| We have recently developed a software framework from which hybrid local search algorithms can be designed automatically. This framework has only been applied to a few problems. The goal of this project would be to extend this framework to other problems, in particular, vehicle routing problems and to compare the results that can be obtained with the methods proposed in the literature. The student will learn to solve combinatorial optimization problems with heuristic algorithms, automatic configuration of optimization algorithms, and the analysis and comparison of optimization algorithms. | We have recently developed a software framework from which hybrid local search algorithms can be designed automatically. This framework has only been applied to a few problems. The goal of this project would be to extend this framework to other problems, in particular, vehicle routing problems and to compare the results that can be obtained with the methods proposed in the literature. The student will learn to solve combinatorial optimization problems with heuristic algorithms, automatic configuration of optimization algorithms, and the analysis and comparison of optimization algorithms. | ||