This thesis concentrates on stochastic programming problems based on empirical and theoretical distributions and their relationship. Firstly, it focuses on the case where the empirical distribution is an independent random sample. The basic properties are shown followed by the convergence between the problem based on the empirical distribution and the same problem applied to the theoretical distribution. The thesis continues with an overview of some types of dependence - m-dependence, mixing, and also more general weak dependence. For sequences with some of these types of dependence, properties are shown to be similar to those holding for independent sequences. In the last section, the theory is demonstrated using numerical examples, and dependent and independent sequences, including sequences with different types of dependence, are compared.

TITLE: Teaching approaches to pupils' concept development about computer programming in SCRATCH AUTHOR: Bc. Hana Šandová DEPARTMENT: Department of information technology and education SUPERVISOR: Doc. RNDr. Miroslava Černochová, CSc. ABSTRACT: This thesis deals with perceptions of pupils about how computers and computer programs work. The main objective is to inquire of pupils (ISCED-2) about how computer and computer programs work, and what ideas influence them. The theoretical part focuses on the history of the use of computers in teaching and an analysis of current approaches to teaching computer science and programming in the Czech Republic and in selected European countries. The practical part is devoted to mapping concepts of pupils aged 11-14 years involved in a teaching experiment organized at two schools in Prague, the aim of which was to examine the impact of teaching programming in "Scratch" over one semester in relation to pupil's ideas about how a computer works and in terms of the development of pupil's computational thinking. KEYWORDS: pupil's imagination, algorithm, programming, computational thinking, Scratch

Tree-based genetic programming (GP) has several known shortcomings: difficult adaptability to specific programming languages and environments, the problem of closure and multiple types, and the problem of declarative representation of knowledge. Most of the methods that try to solve these problems are based on formal grammars. The precise effect of their distinctive features is often difficult to analyse and a good comparison of performance in specific problems is missing. This thesis reviews three grammar-based methods: context-free grammar genetic programming (CFG-GP), including its variant GPHH recently applied to exam timetabling, grammatical evolution (GE), and LOGENPRO, it discusses how they solve the problems encountered by GP, and compares them in a series of experiments in six applications using success rates and derivation tree characteristics. The thesis demonstrates that neither GE nor LOGENPRO provide a substantial advantage over CFG-GP in any of the experiments, and analyses the differences between the effects of operators used in CFG-GP and GE. It also presents results from a highly efficient implementation of CFG-GP and GE.

This thesis deals with a problem of choosing the most suitable agent for a new data mining task not yet seen by the agents. The metric is proposed on the data mining tasks space, and based on this metric similar tasks are identified. This set is advanced as an input to a program evolved by means of genetic programming. The program estimates agents performance on the new task from both the time and error point of view. A JADE agent is implemented which provides an interface allowing other agents to obtain estimation results in real time.

This thesis' topic is stochastic programming, in particular with regard to portfolio optimization and heavy tailed data. The first part of the thesis mentions the most common types of problems associated with stochastic programming. The second part focuses on solving the stochastic programming problems via the SAA method, especially on the condition of data with heavy tailed distributions. In the final part, the theory is applied to the portfolio optimization problem and the thesis concludes with a numerical study programmed in R based on data collected from Google Finance.

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Title: Projectional editor for domain-specific languages Author: Ondřej Dvořák Department: Department of Distributed and Dependable Systems Supervisor: RNDr. Michal Malohlava Abstract: Programming is a craft requiring a good tooling. One of tools selected as crucial for software development is an integrated development environment (IDE) that allows to maintain most of the general-purpose languages. Domain-specific languages grow in a popularity last years, thus it is necessary to support them by IDE as well. Not just a textual or graphical form of DSL sources is suitable for their maintenance, so does the combination of them. One of the promising approaches is represented by a novel method called a projectional editing. Its objective is to show different visualization forms of program source code, combine and manipulate with them at one place. The thought is typically realized by a projectional editor. In this thesis we design a projectional editor for domain-specific languages and provide its experimental implementation. It analyzes potential approaches to a projectional editing and designs their suitable realization in Microsoft Visual Studio. It provides a universal implementation of a projectional editor on the top of Visual Studio as well as on the top of a standalone application. Moreover, it supports...

This thesis is concerned by programming language Python and its suitability of his assignment to the tuition. The work is divided into three dominant parts. The first part describes programming language Python, its elementary characteristics and features. The purpose is to introduce its properties to the reader so that he could estimate if there is point in the deeper concern. There are elements of object-oriented programming in description, too. The second part analyses programming language Python on the basis of ten criteria which are defined considering applicability of the language in preliminary courses of programming. The purpose is to review if the Python is appropriate in these courses. The third part considers the possibilities of the Python tuition at the University of Economics in Prague. The main contribution of the thesis is to give opinion on the fundamental description of the language, to define framework and to pass judgment on the potential chance of use in preliminary courses of programming.

This thesis is focused on optimizing the routes which are implemented in our country at night. Goods are transporting between designated central cities. It deals with creating cyclic routs, along which the goods should be effectively transported, with the respect of the cost. The instruction how to create these paths represents a heuristic method for generating cyclic paths. The algorithm uses the results provided by model that is based on a search for multiple product chart. The chart contains the minimum number of vehicles that provide transport and individual amount of transported goods. The principle of this heuristic method is to create cyclic paths in such a way to be able to serve all transportation requirements with the lowest number of reloads. This approach leads to the fact that the direct paths are preferred.

This thesis aids at comparing two frameworks based on Python and Ruby used for agile web development. Besides the basic description, history and philosophy of both Django and Ruby on Rails, all criteria used for their evaluation are laid down, described and put into wider context. In addition, the implementation of the MVC architecture in both frameworks is described along with the basic project structure and tools available.