Albert Bifet

Artificial Intelligence, Big Data Science, and Machine Learning for Data Streams.

I am a Professor of AI and the Director of the  Te Ipu o te Mahara AI Institute at University of Waikato, and Professor of Big Data at  Data, Intelligence and Graphs (DIG) LTCI, Télécom Paris, IP Paris. I’m co-chair of the NZ AI Researchers Association.

I am leading the  TAIAO Environmental Data Science project, and I’m co-leading the open source project  MOA Massive On-line Analysis.

MIT Press

Machine Learning for Data Streams: with Practical Examples in MOA

  • Series: Adaptive Computation and Machine Learning series
  • Hardcover: 288 pages
  • Publisher: The MIT Press (March 2, 2018)
  • Language: English
  • ISBN-10: 0262037793
  • ISBN-13: 978-0262037792

Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations.