[D&K] IoT Stream Data Mining 2016-2017

Course taught in the Data and Knowledge 2nd year Master Program of Université Paris Saclay
2015-2016

saclay

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

Internships

Internships available here.

Session Labs

  • 1. Lab Massive Online Analysis (MOA) Lab (due 6 January 2017)
  • 2. Lab Multi-Label Classification in Data Streams Lab+Code (due 23 December 2016)
  • Projects

    Propose teams and rank topics by Tuesday 6/12/16: Projects

    Presentation: 10 February 2017 Room F801