Descriptif
Data streams are everywhere, from F1 racing over electricity networks to social media feeds.
Data stream mining or Real-Time Analytics relies on and develops new incremental algorithms that process streams under strict resource limitations.
This course focuses on, as well as extends the methods implemented in open source tools as MOA and River.
Students will learn to how select and apply an appropriate method for a given data stream problem; they will learn how to design and implement such algorithms; and they will learn how to evaluate and compare different solutions.
Data stream mining or Real-Time Analytics relies on and develops new incremental algorithms that process streams under strict resource limitations.
This course focuses on, as well as extends the methods implemented in open source tools as MOA and River.
Students will learn to how select and apply an appropriate method for a given data stream problem; they will learn how to design and implement such algorithms; and they will learn how to evaluate and compare different solutions.
24 heures en présentiel
réparties en:
- Travaux Pratiques : 9
- Leçon : 12
Parcours de rattachement
Format des notes
Numérique sur 20Littérale/grade européenPour les étudiants du diplôme Data & Artificial Intelligence
Le rattrapage est autorisé (Note de rattrapage conservée)- Crédits ECTS acquis : 2.5 ECTS
Le coefficient de l'UE est : 2.5
La note obtenue rentre dans le calcul de votre GPA.