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School of Computer Science

Machine Learning and Optimization






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Maintained by G.Brown
The Manchester MLO Group conducts world-leading research into a wide range of techniques and applications of machine learning, optimization, data mining, probabilistic modelling, pattern recognition and machine perception. The group spans the field from new theoretical developments to large applications, and is currently supported by a number of research bodies, including EPSRC, BBSRC, and several industry partners.

NEWS: older news items...

March 2013: Congratulations to MLO members Ming-Jie Zhao, Narayanan Edakunni, Adam Pocock and Gavin Brown, on their new JMLR paper Beyond Fano's inequality: bounds on the optimal F-score, balanced error rate, and cost-sensitive risk using conditional entropy and their implications .

December 2012: Congratulations to MLO members Joe Mellor and Jon Shapiro on their accepted AISTATS paper Thompson Sampling in Switching Environments with Bayesian Online Change Detection.

August 2012: Congratulations to MLO members Hassan Bashir and Richard Neville on two new papers now available on IEEExplore. A Hybrid Evolutionary Computation Algorithm for Global Optimization, and Convergence measurement in evolutionary computation using Price's theorem, which were published in the IEEE Conference on Evolutionary Computation in June.

June 2012: Congratulations to MLO member Peter Glaus on the publication of a new article in the Journal of Bioinformatics, titled Identifying differentially expressed transcripts from RNA-seq data with biological variation.

Want to study with us for a PhD?

speech recognition, reinforcement learning, ensemble methods, multiobjective optimisation, systems biology, probabilistic models, evolutionary algorithms, image processing, deep neural nets, dynamical systems, boosting, information theory, bayesian methods, online learning, fuzzy systems, speaker identification, optimisation algorithms, semi-supervised learning, unsupervised learning, biochemical networks, neural networks, concept drift, feature selection, game theory, dimensionality reduction,