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: George Hebrail, Jesse Read and Albert Bifet


  • 40% Lab Assignments
  • 60% Project

Lecture Slides

  1. Introduction Slides
  2. Stream Algorithmics Slides
  3. Concept Drift Slides
  4. Classification Slides
  5. Reinforcement Learning Slides

Session Labs

  1. River Lab Source (Submission 25/5/2021)
  2. Lab Massive Online Analysis MOA Source (Submission 1/6/2021)


Select one project before 18/5/2021 Here


Book Machine Learning for Data Streams.