Astrostatistics and Data Mining

'Science with Gaia: how will we deal with a complex billion-source catalogue and data archive?' by Anthony Brown (Leiden University,Netherlads).- 'Recent Advances in cosmological Bayesian model comparison' by Roberto Trotta (University College London, UK).- 'The Art of Data Science' by Matthew Graham (Center for Advanced Computing Research, California Institute of Technology, USA).- 'Astronomical Surveys: from SDSS to LSST' by Robert Lupton (Princeton University, USA).- 'Exoplanet demography, quasar target selection, and probabilistic redshift estimation: Hierarchical models for density estimation, classification, and regression.' by David Hogg (New York University, USA).- 'Learning to disentangle Exoplanet signals from correlated noise' by Suzanne Aigrain (Oxford University, UK).- Astroinformatics and data mining: how to cope with the data tsunami' by Giuseppe Longo (Federico II University, Italy).- Advanced statistical techniques for the processing of astronomical data: time series, images, low number statistics for high energy photons, heteroskedastic data, non-detections.- Challenges in the data mining of astronomical databases: the class imbalance in training sets or how to define prior robust preprocessing for supervised/unsupervised classification robust inference with heterogeneous datasets, how to combine observations, models, priors, etc in a training/test set error propagation.- The challenge of petabyte size databases: scalability, parallel computing, accuracy.- Geometric data organization, sky indexing for efficient data retrieval, intelligent access to petabyte size databases.- Knowledge Discovery in astronomical archives: outlier detection, new object types, parametric inference, model fitting and model selection, etc.- Combining the classical domain knowledge approach with machine learning techniques.- Global approaches for global datasets. The Galaxy zoo and the Universe zoo.- The Virtual Observatories, Data Mining and Astrostatistics: software, standards, protocols.