The Computational and Biological Learning Laboratory uses engineering approaches to understand the brain and to develop artificial learning systems. Research includes Bayesian learning, computational neuroscience, statistical machine learning, and sensorimotor control.

The work of the Machine Learning group is very broad, including all aspects of probabilistic machine learning, ranging from studying fundamental concepts in Bayesian statistics to achieving competitive performance of the group’s algorithms in big-data applications. Topics include machine hearing and vision, information retrieval, learning for control, and bioinformatics.

The work on human learning includes both computational modelling and experimental approaches using robotic and virtual reality interfaces. Using the formal approaches of computational neuroscience, a discipline that studies the nervous system through mathematical models, the group aims to both understand the fundamental organising principles of the brain and to employ these to build more efficient machines. As the superiority of biological systems over machines is rooted in their remarkable adaptive capabilities, the group’s research is focussed on the computational foundations of biological learning.