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Statistical analysis of the gamma-ray bursts satellite data
Bystřický, Pavel ; Mészáros, Attila (advisor) ; Brož, Miroslav (referee)
In this thesis the Gamma-Ray Bursts (GRBs) are studied, the brightest explosions in the universe. GRBs have been observed since year 1967, but there are several unsolved problems. In the first chapter there is an introduction to the issue of GRBs, and the history of observations are briefly described. The Fermi satellite, the latest satellite devoted to gamma-ray burst observations is described in chapter two. Characteristics of the Fermi instruments are also described. The observed data of GRBs are characterized in the third chapter. The distribution of GRB durations, distances, and spectral hardnesses are described. The characteristics of long and short GRBs (distance, isotropy of distribution, metalicity dependence, isotropic energy) are described. A chance of the appearance of a GRB in the Milky Way is discussed. New Fermi observations are described too. Fourth chapter is about models of GRBs. The fireball and canonball models are described. Fifth chapter is focused on the exposure function of CGRO-BATSE, Fermi-GBM, Swift. I have created the exposure function for GBM on Fermi satellite. It is quite difficult, and I have assumed some simplified hypotheses. Information of the satellite's position, position of detectors on the Fermi satellite, have been found on the Fermi web pages and in the article...
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Gradient Boosting Machine and Artificial Neural Networks in R and H2O
Sabo, Juraj ; Bašta, Milan (advisor) ; Plašil, Miroslav (referee)
Artificial neural networks are fascinating machine learning algorithms. They used to be considered unreliable and computationally very expensive. Now it is known that modern neural networks can be quite useful, but their computational expensiveness unfortunately remains. Statistical boosting is considered to be one of the most important machine learning ideas. It is based on an ensemble of weak models that together create a powerful learning system. The goal of this thesis is the comparison of these machine learning models on three use cases. The first use case deals with modeling the probability of burglary in the city of Chicago. The second use case is the typical example of customer churn prediction in telecommunication industry and the last use case is related to the problematic of the computer vision. The second goal of this thesis is to introduce an open-source machine learning platform called H2O. It includes, among other things, an interface for R and it is designed to run in standalone mode or on Hadoop. The thesis also includes the introduction into an open-source software library Apache Hadoop that allows for distributed processing of big data. Concretely into its open-source distribution Hortonworks Data Platform.
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