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. My research focuses on Artificial Intelligence, Big Data Science, and Machine Learning for Data Streams.
I am leading the TAIAO Environmental Data Science project, and I’m co-leading the open source projects MOA Massive On-line Analysis, RIVER, StreamDM for Spark Streaming and SAMOA Scalable Advanced Massive Online Analysis.
This whitepaper discusses current AI capabilities in Aotearoa New Zealand and offers recommendations for establishing Aotearoa New Zealand as a research centre of excellence and trust in AI.
River is a Python library for online machine learning. It is the result of a merger between creme and scikit-multiflow. River’s ambition is to be the go-to library for doing machine learning on streaming data.
China Machine Press and Huazhang Graphics & Information Co., Ltd. has agreed to produce the Chinese edition of the book “Machine Learning for Data Streams: with Practical Examples in MOA” published by MIT Press.
- 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.