The aim of the group is to bring together researchers working on fundamental and applied aspects of artificial learning. MLG researchers deal with datasets coming from various domains including: biomedical, commercial and geographical data; image, sound and text processing; food quality control, etc. This discipline is known, in computer science and other domains, as "Machine Learning", "Data Mining" or "Knowledge Discovery in Databases". The group studies the following topics: - statistical modeling/inference, including sequential information (to predict an information or behavior according to known data) clustering (to discover homogeneous groups in complex and/or high-dimensional data) - supervised classification (to classify previously unseen data based on past experience) - information structuring (to structure the results obtained after a query to a search engine) - time series prediction (to forecast electrical loads, financial series, etc.) - collaborative filtering (for example, to suggest the most adequate product based on purchase records) - data consistency checking and integration (for example, to detect duplicate in a client database) - text mining (digital document processing) Applications of the MLG research results cover various domains, often in collaboration with experts or industrials: genome analysis, text and image analysis, food quality control with spectra, electrical load forecasting, financial data prediction, biochemical networks analysis and pattern matching, study of firm growth trajectories, etc.