Rodrigo Augusto da Silva Alves, Ph.D.

Rodrigo Augusto da Silva Alves, Ph.D.

Ing. Petr Kasalický

Department of Applied Mathematics

Football, the most widely played and followed sport globally, captivates billions of fans worldwide. The significance of predicting match outcomes has garnered attention from statisticians, machine learning researchers, and avid bettors alike. However, while substantial progress has been made in machine learning for outcome prediction, relatively little focus has been placed on forecasting the statistical aspects of the sport. This study aims to address this gap by exploring machine learning methods to analyze and estimate various match statistics as regression problems. Specifically, I investigate six statistics: corners, shots, shots on target, fouls, yellow cards, and red cards. By conducting experiments on four datasets from different football leagues, I evaluate the performance of eight models. My findings reveal that different methods adapt better to certain statistics, and also that some statistics exhibit different behaviors across leagues. Additionally, I observe that certain features, such as the number of corners or shots, are more predictable due to their higher occurrence rates during matches compared to the number of cards.

Thesis on DSpace

Rodrigo Augusto da Silva Alves, Ph.D.

Mgr. Alexander Kovalenko, Ph.D.

Department of Applied Mathematics

This bachelor thesis focuses on the analysis of the triplet problem, whose task is to identify the odd object out of three. There are different methodological approaches to solving this problem, which differ in their basic focus - some methods evaluate which two objects are most similar, while others identify the object that is most dissimilar. The aim of this thesis is to combine these two perspectives and analyze the results. To solve the problem, a neural network has been developed using the TensorFlow library in the Python programming language. The actually research showed that the combination of both approaches did not produce better results than the individual methods alone. The main output of the work is the explanation of how the combination of opposing views affects the final choice of the odd object. For instance, in the set of (Car, Dog and House) one item should be selected as the odd out. House and Car are the most similar or Car is the odd-one-out?

Thesis on DSpace

Rodrigo Augusto da Silva Alves, Ph.D.

Ing. Karel Klouda, Ph.D.

Department of Applied Mathematics

Football is a hugely popular sport with over 3.5 billion fans worldwide, and predicting the outcome of matches has become increasingly important. While several machine learning methods have been used for this purpose, personalized machine learning methods like matrix completion have been neglected. In this thesis, I introduce tensor completion techniques for predicting football match outcomes, using two experimental strands: (1) tensor completion as a prediction method; and (2) tensor completion embeddings extraction. I consider data from five different leagues, four from Europe and one from South America. The results show that tensor completion matches or outperforms other state-of-the-art prediction methods and is capable of improving the performance of Artificial Neural Networks in this task.

Thesis on DSpace

Rodrigo Augusto da Silva Alves, Ph.D.

doc. Ing. Pavel Kordík, Ph.D.

Department of Applied Mathematics

This thesis explores the development of a location-based recommender system. We propose a novel architecture that integrates a general-purpose location encoder with groups of users and their preferences. Our architecture uses separate embeddings for latitude and longitude to dynamically generate recommendations of categories for new Points-of-Interests, such as events, venues, and activities at specific locations. We conduct a series of experiments to demonstrate that the proposed architecture outperforms baseline models. Our models offer more relevant and engaging content that can be used in location-based social networks, where it can increase user engagement and community involvement.

Thesis on DSpace

Rodrigo Augusto da Silva Alves, Ph.D.

doc. Ing. Pavel Kordík, Ph.D.

Department of Applied Mathematics

Fraudsters attempt to camouflage their behavior to remain undetected. This can make it challenging to design models capable of reliably discovering them, as negative samples may be contaminated with hidden positives. Existing research has shown that taking advantage of relationships between instances using graph convolution improves the detection ability. This work proposes a siamese graph neural network that can be trained in a semi-supervised fashion using a small set of known fraudsters. It shows improved performance over existing methods and increased resilience against camouflaged fraudsters.

Thesis on DSpace

Rodrigo Augusto da Silva Alves, Ph.D.

Mgr. Alexander Kovalenko, Ph.D.

Department of Applied Mathematics

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Thesis on DSpace