Machine Learning and Artificial Intelligence

^{Figure : Sports sciences through data sciences (Louvain-la-Neuve Running Heatmap)}

ICTEAM research activities in this field are conducted by seven primary investigators and about fourty researchers. There are two main domains of activity : Machine Learning and Constraint Programming.

Principal Investigators :

Pierre-Antoine Absil, Jean-Charles Delvenne, Yves Deville, Pierre Dupont, John Lee, Siegfried Nijssen, Marco Saerens, Pierre Schaus, Michel Verleysen, Vincent Wertz

Research Labs :

Machine Learning Group, Constraint Group

Research areas :

The research carried out by the UCL Machine Learning Group (MLG) covers both fundamental and applied aspects of machine learning.

Machine learning aims at mining large collection of data and at building models to predict future data. This multidisciplinary field has links to statistics, signal processing, information theory and optimization. It also covers a wide range of applications such as biomedical data analysis, image and video analysis, time series prediction, graph mining, natural language processing, ...

The group specifically addresses the following topics:

High-dimensional, functional and non-linear data analysis
Feature and model selection
Data visualization and manifold learning
Bayesian learning
Biomedical signal processing and analysis, including ECG, EEG, and respiratory signal analysis, and medical image filtering
High-throughput biological data analysis, including microarray data analysis and next-generation sequencing
Temporal series prediction, including electrical workload prediction, financial time-series forecasting, networking measurement prediction
Automata and Grammar induction with application to software system modeling
Structured data analysis, graph mining and collaborative filtering

The UCL Machine Learning Group is organizing, on a yearly basis since 1993, the European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning.

Constraint Programming (CP) is a powerful paradigm for modelling and solving complex combinatorial (optimization) problems. It integrates techniques from artificial intelligence, computer science, operational research and optimization. CP separates the modelling of the problem from the search for solutions. It offers high level modelling languages based on constraints. CP proposes two complementary search mechanisms. Standard CP is based on systematic tree search coupled with pruning techniques to remove infeasable solutions. Constraint-Based Local Search (CBLS) allows heuristic search based on the exploration of neighborhoods. The Constraint Group is mainly interested in consistency techniques, integration of CP and CBLS, graph matching, routing problems, applications in networking, ...

Most recent publications

Below are listed the 10 most recent journal articles and conference papers produced in this research area. You also can access all publications by following this link : see all publications.

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