Modern statistical models are used in statistical learning and statistical engineering to analyse the increased amounts of data collected everywhere on a daily basis in our society. The methods include random forests, regularisation strategies, sparse methods, support vector machines, boot strapping, deep belief networks, and any more. The field is very active with a huge number og journal papers as well as recent text books like 'Elements of Statistical Learning' coming out.
Applications:
- Automation strategies in industrial productions using real time predictions of quality, shape, flaws, etc. (analysis can be based on images, videos or other sensors)
- Educational measurement (text mining, satisfaction surveys, bias studies etc)
- Bioinformatics, gene studies etc. (we collaborate with CBS on such studies)
- Management and business intelligence (text mining, identification of indicators, etc.)
Projects: