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.



CWI Lectures on Machine Learning

  • 2017-11-23T12:00:00+01:00
  • 2017-11-23T18:00:00+01:00
November 23 Thursday

Start: 2017-11-23 12:00:00+01:00 End: 2017-11-23 18:00:00+01:00

Turingroom at Congress Centre Amsterdam Science Park

This year, the CWI Lectures will be held on Thursday 23 November. The theme will be Machine Learning and the afternoon will be organized by CWI's newly established Machine Learning group. 

Speakers will include world leading machine learning researchers:

- Suchi Saria is professor at Johns Hopkins University. Even though she only obtained her Ph.D. (at Stanford) in 2011, her work has already attracted considerable attraction and honours. For example, in 2016 she has been selected as 'one of Popular Science's Brilliant 10'. She has also been named one of AI's "ten to watch" by IEEE in 2014. She is, among many other things, known for successful applications of state-of-the-art theory in machine learning and statistics within the health domain. 
- Neil Lawrence is founder and director of Amazon Research Cambridge and professor at Sheffield University. He is a regular contributor to The Guardian where he writes about impact of machine learning technology to society.   He is former program chair of NIPS, the world's main machine learning conference, and is founder of Data Science Africa.
Max Welling is a research chair in Machine Learning at the University of Amsterdam.  He has secondary appointments at the University of California Irvine and at the Canadian Institute for Advanced Research (CIFAR). He is co-founder of “Scyfer BV” a university spin-off in deep learning. Welling’s machine learning research labs are AMLAB and QUVA Lab.
- Csaba Szepesvári is currently a Professor at the Department of Computing Science of the University of Alberta and a Principal Investigator of the Alberta Innovates Center for Machine Learning. He is best known for the UCT algorithm, which led to a leap in the performance of planning and search algorithms in many domains, in particular in computer go. His research interests include reinforcement learning, statistical learning theory and online learning.

More information about the programme will follow asap.

Registration for the Lectures will open early September.

Members of Machine Learning



Current Projects

  • Machine Learning at the Intrinsic Task Pace
    Deep Spiking Vision: Better, Faster, Cheaper
    Safe Bayesian Inference: A Theory of Misspecification based on Statistical Learning