Students will put their basic machine learning and data analysis knowledge to test for solving practical data science problems in scientific or industrial applications. Typically, we will treat two/three concrete problems coming from scientific or industrial applications (e.g., brain imaging, astrophysics, biology/chemistry, ad placement, insurance pricing). We describe and formalize the motivating problems, discuss the possible solutions, choose one, and assist the students in solving the problem. We will provide data, advise students on their choice of tools, and set up a challenge environment where students can submit their solutions. Evaluation will be based on homework assignments and performance in the challenges. A
Parcours de rattachement
Format des notesNumérique sur 20Littérale/grade réduit
Pour les étudiants du diplôme M1 Innovation, Entreprise, et Société - Voie Innovation technologiqueL'UE est acquise si note finale transposée >= C
- Crédits ECTS acquis : 4 ECTS
La note obtenue rentre dans le calcul de votre GPA.