Course taught in the Data and Knowledge 2nd year Master Program of Université Paris Saclay 2017-2018
The Internet of Things (IoT) is producing huge quantities of data in real-time as data streams. 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. 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.
Lecturers: Jesse Read and Albert Bifet
Evaluation:
- 10% Lab Assignments
- 30% Project
- 60% Final Test
Lecture Slides
- 1. Introduction Slides
- 2. Stream Algorithmics Slides
- 3. Concept Drift Slides
- 4. Evaluation Slides
- 5. Ensembles Slides
- 6. Classification Slides
- 7. Multi-label Streams, Concept Drift and Sequential Data Slides
- 8. Ensembles Slides
- 9. Clustering Slides
Internships
Internships available here.
Session Labs
- 1. Lab Massive Online Analysis (MOA) Lab – Submission (due 10 January 2018)
- 2. Lab Classification in Data Streams (Salle C128): Lab – Code – Submission (due 24 January 2018)
Projects
Propose teams and rank topics by Wednesday 6/12/17: Projects