The thesis describes and analyses the problem of the global financial and economic crisis and its influence on the Russian economy. It constitutes a synthesis of main approaches to the crisis' examination from the years 2008-2011 and develops its own theoretical model for explaining the crisis' emergence and spreading in an open economy, and also analyses in detail the state of the Russian economy and anti-crisis measures. The thesis is based on assumptions that the crisis could have been explained and foreseen theoretically with the help of macroeconomic models, that the anti-crisis policy was in majority of cases inefficient, and that the problems of the Russian economy were caused not only by the global recession, but also by its internal imbalances. On the methodological side, the thesis is based on the ideas of the Austrian and Keynesian economics (the Austrian business cycle model and post- and neo-Keynesian branches) and the financial crises economics and partly on selected thoughts of the Marxian economics, and is divided into four chapters. In the first chapter, the attention is given to the four main heterodox theoretical approaches to explaining financial and economic crises and to the development of a synthetic model. In the second chapter, the reasons, course and main previsions of the U.S. and global crisis are examined and the synthetic model from the first chapter is empirically tested. In the third chapter, the degree of the crisis' influence on different groups of countries is estimated, followed by a description of its main transmission channels and an analysis of anti-crisis measures at the global level. The fourth chapter is dedicated to the specifics of the Russian economy, its development during the crisis and to the anti-crisis policy of the Russian government and of the Central Bank of Russia. The thesis attempts to fill the gap in the existing economic literature by presenting new findings in the above-mentioned areas.

The thesis deals with the analysis of the terms of trade and their impact on the aggregates of the national economy. The main goal was to identify issues of the terms of trade and describe their development from the point of view of total indices and indices for groups of the Standard International Trade Classification (SITC) in the Czech Republic. In the thesis were used time series analysis and measurement of dependence between two quantities. The development of terms of trade indices was highly variable, and the resulting models do not describe too much variability. Total terms of trade index was the most influenced by groups Mineral fuels, lubricants and related materials and Machinery and transport equipment. Gains (losses) from changes in the terms of trade influence the values of aggregates of national economy, their impact on the gross national income and gross disposable income decrease in time. There was also the international comparison of developments of terms of trade indices across the European Union, in the thesis. For this was used cluster analysis by SITC groups. The terms of trade index of the Czech Republic was the most similar to the terms of trade index of Slovakia. The contribution of the thesis is based on the detailed analysis of the terms of trade, which has not been performed before, and describe their relationship with other macroeconomic indicators.

This paper deals with prediction of influence of amino acids mutations on protein stability. The prediction is based on different methods of machine learning. Protein mutations are classified as mutations that increase or decrease protein stability. The application also predicts the magnitude of change in Gibbs free energy after the mutation.

In this thesis we try using machine learning techniques to predict final grade of a student in a learning management system on the basis of his behavior during the semester. The aim is to determine the optimal technology for the extraction, treatment and machine learning on data. The whole system would then be implemented as a module that we will be able to plug in the existing system.

The main goal of this thesis was to get acquainted with the phases of data mining, with the support of the programming languages Python and R in the field of data mining and demonstration of their use in two case studies. The comparison of these languages in the field of data mining is also included. The data preprocessing phase and the mining algorithms for classification, prediction and clustering are described here. There are illustrated the most significant libraries for Python and R. In the first case study, work with time series was demonstrated using the ARIMA model and Neural Networks with precision verification using a Mean Square Error. In the second case study, the results of football matches are classificated using the K - Nearest Neighbors, Bayes Classifier, Random Forest and Logical Regression. The precision of the classification is displayed using Accuracy Score and Confusion Matrix. The work is concluded with the evaluation of the achived results and suggestions for the future improvement of the individual models.

This paper is focus on flowers recognition in an image and class classification. Theoretical part is focus on problematics of deep convolutional neural networks. The practical part if focuse on created flowers database, with which it is further worked on. The database conteins it total 13000 plant pictures of 26 spicies as cornflower, violet, gerbera, cha- momile, cornflower, liverwort, hawkweed, clover, carnation, lily of the valley, marguerite daisy, pansy, poppy, marigold, daffodil, dandelion, teasel, forget-me-not, rose, anemone, daisy, sunflower, snowdrop, ragwort, tulip and celandine. Next is in the paper described used neural network model Inception v3 for class classification. The resulting accuracy has been achieved 92%.

The aim of this master thesis is to design and implement a system for the detection, tracking and classification of vehicles from streams or records from traffic cameras in language C++. The system runs on the platform Robot Operating System and uses the OpenCV, FFmpeg, TensorFlow and Keras libraries. For detection cascade classifier is used, for tracking Kalman filter and for classification of the convolutional neural network. Out of a total of 627 cars, 479 were tracked correctly. From this number 458 were classified (trucks or lorries not included). The resulting system can be used for traffic analysis.

In this diploma thesis, the reader is introduced to artificial neural networks and convolutional neural networks. Based on that, the design and implementation of a new library for convolutional neural networks is described. The library is then evaluated on widely used datasets and compared to other publicly available libraries. The added benefit of the library, that makes it unique, is its independence on data types. Each layer may contain up to three independent data types - for weights, for inference and for training. For the purpose of evaluating this feature, a data type with fixed point representation is also part of the library. The effects of this representation on trained net accuracy are put to a test.

The diploma thesis is focused on evalution of efficiency and risks of the project implemnted by the municipality. The theoretical part defines the issue of building investments, public project, and indicators of economic efficiency of investments. Risk analysis is described here. The practical part is focused on the economic evaluation of the effectiveness and risk analysis of the project implemented by the municipality.

This master thesis deals with classification of sleep stages on the base of polysomnographic signals. On several signals was performed analysis and feature extraxtion in time domain and in frequency domain as well. For feature extraxtion was used EEG, EOG and EMG signals. For classification was selected classification models K-NN, SVM and artifical neural network. Accuracy of classifation is different depending on used method and spleep stages split. The best results achieved classification among stages Wake, REM, and N3, with neural network usage. In this case the succes was 93,1 %.