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: Maroua Bahri, George Hebrail, Jesse Read and Albert Bifet
Evaluation:
- 40% Lab Assignments
- 60% Project
Lecture Slides
- Classification and Ensembles. Slides
- Time series analytics
- Reinforcement Learning Slides
- Data Driven Predictive Maintenance Slides Part I and Slides Part II
Session Labs
- River Lab Source (Submission 22/3/2022)
- Lab Massive Online Analysis MOA Source (Submission 5/4/2022)
Projects
Projects: Here
To be presented 05/04/2022
References
Book Machine Learning for Data Streams.