Leader of the group Machine Learning: Peter Grunwald.

Our research group focuses on how computer programs can learn from and understand data, and then make useful predictions based on it. These algorithms integrate insights from various fields, including statistics, artificial intelligence and neuroscience.  

 Machine-learning applications are increasingly part of every aspect of life, from speech recognition on cell phones to illness prediction in healthcare. One common problem is extremely polluted data, for which no single model can provide adequate explanations. At CWI we address this issue with statistical machine learning based on combining predictions from different models and experts in order to achieve reliable conclusions.

We also study how networks of neurons in the brain process information, and how modern deep-learning methods can benefit from neuroscience. We develop novel neural networks, like Deep Adaptive Spiking Neural Networks, and also theoretical models of neural learning and information processing in biology. Applications of our work range from low-energy consumption neural machine learning to neuroprosthetics, to increased insight into the question of how the brain works.



Current events

ML Seminar: Tristan Cossio (Netflix)

  • 2018-07-19T11:00:00+02:00
  • 2018-07-19T12:00:00+02:00
July 19 Thursday

Start: 2018-07-19 11:00:00+02:00 End: 2018-07-19 12:00:00+02:00

CWI, Lecture room L016

With pleasure we announce this seminar: Data Science at Netflix

Data science at Netflix is much more than just personalized recommendations (e.g. the 2006-2009 Netflix Prize) - it is also a
critical aspect of streaming, quality control, and content development and buying decisions.  This talk surveys the breadth of data science work at Netflix, with a specific focus on how the Content Science & Algorithms team (based in Hollywood CA) uses machine learning for valuation and programming of Licensed & Original content.


Associated Members



Current projects with external funding

  • Deep Spiking Vision: Better, Faster, Cheaper (DEVIS)
  • Efficient Deep Learning Platforms (eDLP)
  • Enabling Personalized Interventions (EPI)
  • Safe Bayesian Inference: A Theory of Misspecification based on Statistical Learning (SAFEBAYES)

Related partners

  • Philips
  • KPMG
  • SURFsara B.V.
  • Universiteit van Amsterdam
  • Vrije Universiteit Amsterdam