Machine Learning for Data Science (MLDS)
In the era of data science, it is more than ever necessary to address several challenges by Machine Learning techniques. The assessment and development of new unsupervised, semi-supervised or supervised learning methods, and sophisticated visualizations are needed.
The team MLDS at LIPADE has a long experience in this domain. It has all the necessary skills in applied mathematics and computer science to address different goals in MLDS with various approaches such as Mixture models, Factorization, graph Networks, data-visual analytics and ensemble methods. The research works of the team are realized in theoretical and applied contexts including bioinformatics, text-mining, recommender systems and computer vision. Mathematical models and algorithms are proposed to efficiently reach the objective of knowledge discovery from large and high-dimensional data.