Statistical Learning Theory for Modern Machine Learning – ICTP Colloquium

John S Shawe-Taylor is a professor at University College London (UK). His main research area is Statistical Learning Theory. His contributions range from Neural Networks, to Machine Learning, to Graph Theory.
John Shawe-Taylor obtained a PhD in Mathematics at Royal Holloway, University of London in 1986. He subsequently completed an MSc in the Foundations of Advanced Information Technology at Imperial College. He was promoted to Professor of Computing Science in 1996. He has published over 150 research papers. He moved to the University of Southampton in 2003 to lead the ISIS research group. He has been appointed the Director of the Centre for Computational Statistics and Machine Learning at University College, London from July 2006. He has coordinated a number of European wide projects investigating the theory and practice of Machine Learning, including the NeuroCOLT projects. He is currently the scientific coordinator of a Framework VI Network of Excellence in Pattern Analysis, Statistical Modelling and Computational Learning (PASCAL) involving 57 partners.

Machine learning aims to identify properties of a distribution from a set of examples. The presentation introduces the statistical learning framework for analysing this task, focussing on the PAC-Bayes approach that draws on Bayesian inference and Probably Approximately Correct learning. The application of this analysis to modern machine learning approaches including Kernel methods and deep learning will be presented, highlighting successes and ongoing shortcomings of these results.