GREAT Summer School on Astrostatistics and Data Mining

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The objective of the summer school is to provide students with both, a wide overview of the body of statistical and data mining techniques widely applied to astronomical problems, and a specialized primer to the latest developments in this field, occurred in the past decade. A preliminary program of the school is as follows: Classical statistics: basic concepts, parameter estimation, statistical inference, error analysis, hypothesis testing, confidence intervals. The frequentist aproach versus Bayesian statistics. Priors. The problem of model selection. Sampling techniques (MCMC, nested sampling...). Latest developments in Bayesian inference for Astronomy Advanced statistical techniques: Time series analysis. Wavelet analysis. Statistical techniques for astronomical image processing. Spherical statistics. Supervised Classification and Regression: the problem of feature selection, the curse of dimensionality. Regression methods. Assessment of regression models. Continuous and categorical variables. Classification models (artificial neural networks, support vector machines, bayesian networks...). Model evaluation (n-fold cross validation and variants; statistical tests). Feature selection revisited. The construction of training and test sets in astronomical applications of Data Mining. Unsupervised classification: alternative methodologies, the problem of feature selection for clustering, evaluation. Data Mining and Statistics in the era of the petabyte databases. Technical aspects, database architecture, intelligent access, distributed computing, efficient software Basic knowledge of statistics is desirable. The lectures will be interspersed with practical exercises. Students will be asked to bring their laptops with appropriate software installed.