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Machine Learning

Machine learning is the study of computational processes that find patterns and structure in data. Our group is interested in a broad range of theoretical aspects of machine learning as well as applications. Much of the current excitement around machine learning is due to its impact in a broad range of applications. The applications considered in our research include astronomy, systems biology, neuroscience, natural language processing, robotics, and computer vision.

Faculty

  • Chris Williams: Gaussian processes, image interpretation, unsupervised learning, deep learning, time series models
  • Amos Storkey: Continuous time systems, deep learning, stochastic differential equations
  • Iain Murray: Bayesian statistics, approximate inference, Markov chain Monte Carlo, scientific data analysis
  • Guido Sanguinetti: probabilistic modeling of biological systems, dynamics of regulatory networks, computational epigenetics, spatiotemporal systems
  • Charles Sutton: probabilistic modeling of large-scale computer systems, approximate inference, statistical processing of natural and programming languages
  • Nigel Goddard: probabilistic modeling of energy-related systems
  • Chris Bishop: Graphical models, variational methods, pattern recognition

Events

 
We have two journal reading groups: PIGS and PIGlets. We also have a weekly brainstorm coffee and machine learning lunch.

Joining the group

 
If you would like to join the machine learning group as a PhD student, please see this information for prospective postgraduates.
 
We also have a large MSc programme in machine learning.  For this, you should apply directly to the School for Informations.  Please see this Information about the MSc programme.
 
Occasionally we have openings for postdoctoral researchers. Please contact the individual lecturers directly about this.
 

Classes 

 
As part of our MSc programme, we teach a large number of classes in machine learning, namely:
 
 

Related Research @ Edinburgh

 
Many other research groups at Edinburgh work actively in related areas, including ILCC (statistical natural language processing), IPAB (vision and robotics), ICSA (self-managing compilers and computer systems), BioSS (bioinformatics, statistics), and the School of Mathematics (statistics). Some of these links are represented by the Informatics Research Programme on Machine Learning.

Funding

 
We receive funding for our research from many sources, including: