The thesis called ?A child at primary school suffering from a learning disability from a teacher´s point of view? is focused on scrutinizing this issue and looking at how the teachers deal with these children in real everyday classwork. The basic research questions are: ?What is a general attitude of primary school teachers to learning disabilities? How do they deal with children suffering learning disabilities during the classwork with a regard to their personal attitudes to this problem? ?

The main goal of our thesis is to make the implementation of a classification method called linear discriminant analysis more efficient. It is a model of multivariate statistics which, given samples and their membership to given groups, attempts to determine the group of a new sample. We focus especially on the high-dimensional case, meaning that the number of variables is higher than number of samples and the problem leads to a singular covariance matrix. If the number of variables is too high, it can be practically impossible to use the common methods because of the high computational cost. Therefore, we look at the topic from the perspective of numerical linear algebra and we rearrange the obtained tasks to their equivalent formulation with much lower dimension. We offer new ways of solution, provide examples of particular algorithms and discuss their efficiency. Powered by TCPDF (www.tcpdf.org)

Title: Deep neural networks and their application for economic data processing Author: Bc. Tomáš Witzany Department: Department of Theoretical Computer Science and Mathematical Logic Supervisor: Doc. RNDr. Iveta Mrázová, CSc., Department of Theoretical Com- puter Science and Mathematical Logic Abstract: Analysis of macroeconomic time-series is key for the informed decisions of national policy makers. Economic analysis has a rich history, however when considering modeling non-linear dependencies there are many unresolved issues in this field. One of the possible tools for time-series analysis are machine learn- ing methods. Of these methods, neural networks are one of the commonly used methods to model non-linear dependencies. This work studies different types of deep neural networks and their applicability for different analysis tasks, including GDP prediction and country classification. The studied models include multi- layered neural networks, LSTM networks, convolutional networks and Kohonen maps. Historical data of the macroeconomic development across over 190 differ- ent countries over the past fifty years is presented and analysed. This data is then used to train various models using the mentioned machine learning methods. To run the experiments we used the services of the computer center MetaCentrum....

Utilization of additional information of the pulse for ALS data classification in ragged terrain Abstract The diploma thesis deals with airborne laser scanning filtering problem in sandstone landscape which is characterized by ragged terrain and in our country also by dense vegetation that makes difficult to transit laser pulse to terrain that can lead to lower accuracy of created DTM. In the first part the basic filtering algorithm that are systematic divided into several groups are described. The emphasis is also put on theoretic problems which we have to deal with during the filtering of laser scanner data acquired in sandstone landscape. The main goal of the thesis is to suggest changes in one of the existing algorithm to additional information of the pulse (mainly amplitude and width of the pulse) be used, and to test this method over the real data. At the end the results of the method and its implementation are critically evaluated. Keywords: airborne laser scanning, point cloud segmentation, point cloud classification, sandstone landscape, DTM

At present companies are facing to a problem of treating huge amount of company`s content. There is needed to provide correct metadata to the documents to enable its qualified classifying during a whole life cycle of the document. The target of this diploma thesis puts emphasis on a proposal of the classifying methodology of the customer`s documents, while intercepting a proposal of the process of classifying, testing and support or even enhancement. Topic of this thesis is unique while it is based on the combination of the IT and librarian`s technologies. Methodology also consists a proposal of a subject retrieval language and a proposal of IT services, which are going to ensure the quality of the provided classification. The thesis is devided into two parts. The theoretical one explains the basic terms, theory of classifying and standards, legislature, which all together have the important influence on the field. The practical part includes itself the methodology.

The aim of this study is to analyse the portfolio of the pharmaceutical company Meda Pharma s.r.o. and then, according to the results, suggest a marketing stratégy to be effective up to 2020. The work has two parts, theoretical and practical. The theoretical part explains the basic concepts related to the market, marketing, marketing mix, marketing strategy and its instruments. This part is also dedicated to concepts relating to the pharmaceutical industry. The practical part is focused on analysis of portfolio of the company. I will use the marketing mix, the Boston matrix, evaluate the largest competitors in each product and focus also on the profitability of products of the company. In conclusion, I will propose a marketing strategy to be put in place by 2020.

The aim of the thesis is to classify european countries in terms of their business climate using the method of cluster analysis over the years 2008-2013. Changes in classification during this period are associated with events of global significance (e.g. World financial crisis) or local importance (reforms, EU strategy...). Data base consists of indicators describing administrative, financial and law environment for doing business and are publicated by World Bank. Clusters indicate both geographic conditionality and specific attributes of these clusters, which suggest countries with better/worse conditions in some of the areas. Particular attention is given to development in the Czech republic. There was significant change in classificiation between 2008 and 2009 and subsequently became stable. The results of analysis correspond to the existing rankings and indicators of business demography. Potentials for improvement which might leed to stabel economic development according to the conducted analysis are outlined in the end of the thesis (f.e. implementation of unified administrative points, electronization and further simplification of bureaucratic processes).

This thesis focuses on comparison of selected data mining methods for solving classification tasks with the method of logistic regression. First part of the thesis briefly introduces data mining as a scientific discipline and classification task is shown in the context of knowledge data discovery. Next part explains the principle of particular methods amongst which, along with logistic regression, artificial neural networks, classification decision trees and Support Vector Machine method were selected. Together with mathematical background of each algorithm, demonstration of how the classification functions for new examples is mentioned. Analytical part of this thesis tests decribed methods on real-world data from the Lending Club company and they are compared based on classification accuracy. Towards the end, an evaluation of logistic regression is made in terms of whether its majority position is due to historical reasons or for its high classification accuracy compared to other methods.

The aim of the thesis is to analyze the current state of the international hotel industry and possibility of using international experience for the development of the hotel industry in Russia. The thesis is divided into three chapters. In the first chapter the hotel industry is examined from a theoretical point of view. The second chapter examines the trends of the international hotel industry, including the role of international hotel chains in the world. The third chapter is focused on the analysis of the influence of international hotel chains on the development and improvement of the hotel industry in Russia.

Automated recognition of selected terrain features from their cartographic representation. This diploma thesis is dedicated to automatic classification of selected terrain shapes and their cartographic representation. The main aim of this thesis is to design methodological approach for automatic recognition of terrain shapes (hills and valleys) with the use of Machine Learning (Deep Learning). The first part of suggested method divides rough terrain segmentation into two categories, which will be then classified with convolutional neural network. The second part of the thesis is dedicated to the very classification of pre-segmented terrain shapes using Machine Learning. Both parts of the processing are using photos SRTM30 as an input data. The whole proposed method was developed in Python programming language with the usage of Arcpy, TensorFlow and Keras libraries. Keywords: Digital cartography, GIS, terrain shapes, Machine Learning, Deep Learning, recognition, classification, segmentation