Course taught in the Data and Knowledge 2nd year Master Program of Université Paris Saclay 2019-2020
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: Jacob Montiel, Heitor Gomes, Jesse Read and Albert Bifet
Evaluation:
- 10% Lab Assignments
- 30% Project
- 60% Final Test
Lecture Slides
- 1. Introduction Slides
- 2. Scikit-multiflow Slides
- 3. Concept Drift Slides
- 4. Clustering Slides
- 5. Classification Slides
- 6. Ensembles Slides
- 7. Time Series Slides
- 8. Stream Algorithmics Slides
Session Labs
- 1. Lab Sckit-multiflow (Salle 1A252) Lab – Submission (due 17 December 2019)
- 2. Lab Massive Online Analysis MOA (Salle 1A252) Lab – Submission (due 21 January 2020)
Projects
Project Presentations (31/01/2020): Projects
Exam
Exam 27/01/2020: You can bring any non-electronic documents to the exam.
References
Book Machine Learning for Data Streams.