UE Data Challenges

Diplômes intégrant cet élément pédagogique :

Descriptif

Face up challenging real-world problems in machine learning, be involved in multidisciplinary teams of data scientists, computer scientists, mathematicians and expert students in signal processing, and contribute to leading your team to the top rank!

Different teams with M2 students issued from either MSIAM Data Science, MoSIG Data Science and SIGMA work on a same challenge on either complex, structured or big data, and maybe a combination of all three. Try and compare different approaches, take benefit from the computational power of clusters and from advice of your supervisors.

The data challenges stretch on several months, include some tutored sessions, if needed mini-courses, and of course your regular involvement over that period of time.

Evaluation :
The final mark in composed as 1/3 score (ranking/performance), 1/3 report and 1/3 oral presentation.

Pré-requis

Elementary notions in probability theory (multivariate distributions), machine learning (concepts of regression, classification and clustering) and programming (usually python, although other languages may be chosen).

Compétences visées

At the end of the course, the student will be able to work in teams involving various skills (machine learning, statistical modelling, programming, data bases and others). They will acquire skills in data analysis and self-training in acquiring or reinforcing skills among the four listed above.

Bibliographie

Dopplick, R. Expanding minds to big data and data sciences. Inroads, 6(3) 88, 2015.

Yang, J. How we did it: Jie and Neeral on winning the first Kaggle-in-class competition at Stanford, 2010.

Informations complémentaires

Méthode d'enseignement : En présence
Lieu(x) : Grenoble - Domaine universitaire
Langue(s) : Anglais