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Scientific Applications of Machine Learning



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UCI ICS IGB SISL Scientific Applications of Machine Learning Eric Mjolsness Scientific Inference Systems Laboratory Donald Bren School of Information and Computer Sciences and Institute for Genomics and Bioinformatics University of California Irvine UCI ICS IGB SISL Scientific Imagery Applications NGC 7331 http photojournal jpl nasa gov catalog PIA063 Arabidopsis SAM Meyerowitz Lab UCI ICS IGB SISL Some Basic Machine Learning Distinctions Supervised vs unsupervised learning Supervised e g classification and regression Feature selection regression for phenomenological model fitting e g GRN s Unsupervised e g clustering may be preprocessor Generative vs Kernal methods Generative statistical inference models Kernal methods e g Support Vector Machines Vector vs Relationship data Vector data preprocessed image features log I x Images time series shifted spectra semigroup actions Sparse graph relationship data permutation actions UCI ICS IGB SISL Correspondence Problems Extended sources map morphologies Similar to biological imaging problems Fewer sources but many pixels Moving or changing point sources E g Ida and Dactyl JPL MLS Dense point sources with instrument noise e g globular clusters radial density function Techniques soft permutations geometric transformations via optimization continuation Embedding inside a graph clustering optimization algorithm Multiscale acceleration of optimization UCI ICS IGB SISL Mixture Models Mixture of Gaussians t distributions Can do outlier detection Mixture of factor analyzers Utsugi and Kumagai 2000 Mixture of time series models Problem specific generative models Frey et al 1998 Can formulate with a Stochastic Parameterized Grammar Clustering graphs UCI ICS IGB SISL Stochastic Grammars for Data Modeling UCI ICS IGB SISL Text Biology Models UCI ICS IGB SISL More Detailed Clustering Grammars Clusters generate data Priors on cluster centers variances Iterative through levels in a hierarchy Recursive through hierarchy Datum 1 1 x



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