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


  • 40% Lab Assignments
  • 60% Project

Lecture Slides

  1. Classification and Ensembles. Slides
  2. Time series analytics
  3. Reinforcement Learning Slides
  4. Data Driven Predictive Maintenance Slides Part I and Slides Part II

Session Labs

  1. River Lab Source (Submission 22/3/2022)
  2. Lab Massive Online Analysis MOA Source (Submission 5/4/2022)


Projects: Here

To be presented 05/04/2022


Book Machine Learning for Data Streams.