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Personal profile

Research interests

I am based in the Bath Institute for Mathematical Innovation, where I have close links with both the Department of Mathematical Sciences and the Department of Computer Science. My research interests centre on Machine Learning, and extend to Data Science and Artificial Intelligence – all viewed from a probabilistic modelling and Bayesian statistical perspective wherever possible. In fact, I have a general interest in pursuing probabilistic solutions to traditionally non-probabilistically framed problems in machine learning and beyond.

Some specific topics of current focus are:

  • Sparse Bayesian models (the “relevance vector machine”) and related novel learning techniques
  • Probabilistic approaches to tree-based pattern recognition
  • Adaptive analysis of multivariate time series
  • Methods for intelligent statistical automation
  • New perspectives on deep neural networks
  • Model-driven data mapping and visualisation techniques

Having spent several years in industry, I also maintain an interest in some specific machine learning application areas. These include medical diagnostics, automotive modelling, payment fraud detection, sports performance analysis, and interactive entertainment (where I conceived and built the “Drivatar” AI technology for the long-running Microsoft Xbox franchise “Forza Motorsport”).

Further information may be found at my personal website.

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Research Output 2001 2001

Sparse Bayesian Learning and the Relevance Vector Machine

Tipping, M. E., Jun 2001, In : Journal of Machine Learning Research. 1, p. 211-244 34 p.

Research output: Contribution to journalArticle

Open Access
Relevance Vector Machine
Bayesian Learning
Support vector machines
Basis Functions
Support Vector Machine