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Recommended Courses and Workshops

On this page you find courses and workshops which are recommended by ENBIS. The recommendations come from our members and sponsors. So if you have a recommendation, please feel free to contact us .

 Applied Bayesian Statistics School

BAYESIAN MACHINE LEARNING WITH BIOMEDICAL APPLICATIONS


   June, 11 - 15, 2010 - Bolzano/Bozen, Italy


Lecturer

Prof. David B. Dunson
Department of Statistical Science, Duke University Durham, NC, USA

Programme and registration details are available at

>>>>   www.mi.imati.cnr.it/conferences/abs10.html    <<<<

Interested people are invited to contact the ABS10 Secretariat at

                     abs10@mi.imati.cnr.it

---------------- COURSE OUTLINE ---------------------------------------

This short course is intended to provide a practically-motivated introduction to Bayesian methods for machine learning and high-dimensional data analysis. Some topics of particular interest include high-dimensional variable selection for regression and classification, and multi-task learning and combining of information for related signals, functions or images. A brief overview will be provided of Bayesian methods for linear regression with very many predictors using shrinkage priors and mixture priors. This overview will include Bayesian formulations of Lasso, elastic net and relevance vector machine (RVM) methods that have been widely used in the literature. In addition, spike and slab mixture priors for formal Bayes subset selection and model averaging will be presented. We also describe sparse Bayesian latent factor regression methods, which can accommodate selection of correlated sets of predictors in large p, small n settings. Computational methods will be described based on maximum a posteriori estimation and Markov chain Monte Carlo algorithms. The methods will be compared through simulation studies and applied to a variety of data examples, including machine learning data and biomedical applications involving gene expression and other high-dimensional markers. An emphasis will be on practical issues in implementing and interpreting results, and code will be provided in R and Matlab.

The school will make use of lectures, practical sessions, software demonstrations, informal discussion sessions and presentations of research projects by school participants. The slides and background reading material will be distributed to the students before the start of the course.
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Guido Consonni and Fabrizio Ruggeri
ABS10 Directors