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Artificial Intelligence in Bang! Game
Kolář, Vít ; Lodrová, Dana (referee) ; Orság, Filip (advisor)
The goal of this master's thesis is to create an artificial intelligence for the Bang! game. There is a full description of the Bang! game, it's entire rules, player's using strategy principles and game analysis from UI point of view included. The thesis also resumes methods of the artificial intelligence and summarizes basic information about the domain of game theory. Next part describes way of the implementation in C++ language and it's proceeding with use of Bayes classification and decision trees based on expert systems. Last part represent analysis of altogether positive results and the conclusion with possible further extensions.
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Data Mining with Python
Šenovský, Jakub ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
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.
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Artificial Intelligence Approaches for Filtering of Spams
Matula, Tomáš ; Žádník, Martin (referee) ; Schwarz, Josef (advisor)
This thesis focuses on the e-mail classification and describes the basic ways of spam filtering. The Bayesian spam classifiers and artificial immune systems are analyzed and applied in this thesis. Furthermore, existing applications and evaluation metrics are described. The aim of this thesis is to design and implement an algorithm for spam filtering. Ultimately, the results are compared with selected known methods.
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The Use of Means of Artificial Intelligence for the Decision Making Support on Stock Market
Hrach, Vlastimil ; Budík, Jan (referee) ; Dostál, Petr (advisor)
The diploma thesis deals with artificial intelligence utilization for predictions on stock markets.The prediction is unconventionally based on Bayes' probabilistic model theorem and on its based Naive Bayes classifier. I the practical part algorithm is designed. The algorithm uses recognized relations between identifiers of technical analyze. Concretely exponential running averages at 20 and 50 days had been used. The program output is a graphic forecast of future stock development which is designed on ground of relations classification between the identifiers
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Context-Aware Notification Filter for Android
Jaklovský, Samuel ; Špaňhel, Jakub (referee) ; Szentandrási, István (advisor)
The goal of this thesis is to develop an application for devices running Android which will determine user profile, based on obtained context, and apply user pre-defined sound settings for this profile. The thesis contains a description of common theory and design of user interface which was implemented as fully operational application. The application uses Naive Bayes classifier and Decision tree for determining the user profile. The functionality of the application was successfully tested by twenty users. The average ratings in the questionnaires were about eight and a half points from a possible maximum of ten. These results can be considered successful.
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Robust Student estimator
Rázek, Stanislav ; Friml, Dominik (referee) ; Dokoupil, Jakub (advisor)
The diploma thesis deals with the formulation of the algorithm for estimating the parameters of the linear ARX model with Student's noise using approximate Bayesian inference. The topics of Student's noise, Approximate Bayesian inference and Student's algorithm are discussed. The formulated parameter estimation algorithm is compared with other model parameter estimation methods and evaluated. At the same time, the Student's filter is derived and its connection with the Kalman filter is discussed.
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What explains different duration of the Great Recession across countries?
Petrů, Vojtěch ; Baxa, Jaromír (advisor) ; Hlaváček, Michal (referee)
The research concerning differences in duration of the Great Recession is limited and inconclusive. We define duration of crisis as the count of years lost due to the crisis, and estimate the determinants of crisis duration on the dataset of 54 developed and developing countries. This thesis contrasts with previous literature by employing Bayesian Model Averaging (BMA) to accommodate for the large amount of potential explanatory variables and to address model uncertainty. Moreover, an innovative measure of export competitiveness, which accounts for the changes in non-price factors such as quality, is used. The results bring suggestive evidence of positive impact of developed financial markets, high share of private consumption and improvements in export competitiveness. We also find positive effect of fiscal policy stimulus once it is controlled for the feedback loop of uncertainty which appears when heavily indebted countries finance fiscal stimulus through issuance of additional debt. Lastly, it needs to be concluded, that the results are not robust to all prior specifications. In particular, the more restrictive Beta binomial model prior shrinks the statistical significance of aforementioned results heavily. JEL Classification F12, F21, F23, H25, H71, H87 Keywords Great Recession, Crisis duration, Economic...
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Data Mining with Python
Šenovský, Jakub ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
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.
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Context-Aware Notification Filter for Android
Jaklovský, Samuel ; Špaňhel, Jakub (referee) ; Szentandrási, István (advisor)
The goal of this thesis is to develop an application for devices running Android which will determine user profile, based on obtained context, and apply user pre-defined sound settings for this profile. The thesis contains a description of common theory and design of user interface which was implemented as fully operational application. The application uses Naive Bayes classifier and Decision tree for determining the user profile. The functionality of the application was successfully tested by twenty users. The average ratings in the questionnaires were about eight and a half points from a possible maximum of ten. These results can be considered successful.
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Artificial Intelligence Approaches for Spam Detection
Vránsky, Radovan ; Sekanina, Lukáš (referee) ; Schwarz, Josef (advisor)
This thesis deals with various methods used for spam detection and identification. In the introduction various methods are described. Then Bayes' theorem and methods for spam detection that use this theorem are described in detail. This section also discusses biological and artificial immune systems and methods for spam detection based on artificial immune systems. Next sections contain the description of custom spam detection system design and implementation. Finally the system is tested and the results are evaluated.
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