Mark Hoogendoorn’s Book Published: Machine Learning for the Quantified Self
Mark Hoogendoorn (Assistant Professor of Artificial Intelligence within the Computational Intelligence group of the Department of Computer Science at the VU Amsterdam), alongside Burkhardt Funk (Leuphana University Lüneburg), have written a book titled: “Machine Learning for the Quantified Self – On the Art of Learning from Sensory Data”, which has recently been published by Springer.
Self-tracking has become part of the modern lifestyle, and the amount of data generated by these devices is so overwhelming that it is difficult to obtain useful insights from it. This book explains the complete loop to effectively use self-tracking data for machine learning. While it focuses on self-tracking data, the techniques explained are also applicable to sensory data in general, making it useful for a wider audience. Discussing concepts drawn from state-of-the-art scientific literature, it illustrates the approaches using a case study of a rich self-tracking data set. While there are ample books that explain machine-learning techniques, self-tracking data comes with its own difficulties that require dedicated techniques such as learning over time and across users. More information about the book can be found HERE
CWI researcher Peter Boncz has been appointed as one of the 2022 fellows of the Association of Computing Machinery (ACM).
The ADS Thesis Award winners for 2022 are Philipp Sommerhalter and Sarah Kwakkelaar in the BSc category and Krijn Doekemeijer and Julia Sudnik for the MSc category. The winners were announced at the ADS Highlights Event on the 8th of December 2022!
A symposium entitled ‘From Systems and Networks to Complex Cyber Infrastructures’ took place on 9th December 2022 in honor of Cees de Laat (UvA IvI) and Leon Gommans (UvA IvI).