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Utilising Large Pretrained Language Models for Configuration and Support of a Clinical Information System
Sova, Michal ; Burget, Radek (referee) ; Rychlý, Marek (advisor)
The aim of this work is to get acquainted with the essence and use of large pre-trained language models, to get acquainted with the configuration options of the clinical information system FONS Enterprise and the possibility of its adaptation to the specific environment of customers. The work first presents large pre-trained language models and the FONS Enterprise clinical information system. This work examines possibilities of training models and implementing RAG methods on data from the clinical system. The implementation of the RAG architecture is supported by the tools LangChain and LlamaIndex. The results show that the RAG method with the Gemma model and the bge-m3 embedding model provides the most relevant answers on basic questions, but struggles to understand more complex questions. The method of pre-training the model does not produce the expected results, even after adjusting the training parameters.
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Strategic Game with Uncertainity
Sova, Michal ; Zbořil, František (referee) ; Zbořil, František (advisor)
The thesis focuses on creating an autonomous system for the game Scotland Yard by using machine learning method. The problem is solved by algorithm Monte Carlo tree search. Algorithm Monte Carlo tree search was tested against algorithm Alpha-beta. These results showed that Monte Carlo tree search algorithm is operational but win rate of this algorithm is lower than win rate of algorithm Alpha-beta. The resulting system is functional, autonomous and capable of playing the game Scotland Yard on simplified game area. There was an attempt to expand simplified version of the game Scotland Yard. In expanded version algorithm Alpha-beta was not successful because of insufficient computational resources. Algorithm Monte Carlo tree search, on the other hand, was more successful in expanded version.
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Strategic Game with Uncertainity
Sova, Michal ; Zbořil, František (referee) ; Zbořil, František (advisor)
The thesis focuses on creating an autonomous system for the game Scotland Yard by using machine learning method. The problem is solved by algorithm Monte Carlo tree search. Algorithm Monte Carlo tree search was tested against algorithm Alpha-beta. These results showed that Monte Carlo tree search algorithm is operational but win rate of this algorithm is lower than win rate of algorithm Alpha-beta. The resulting system is functional, autonomous and capable of playing the game Scotland Yard on simplified game area. There was an attempt to expand simplified version of the game Scotland Yard. In expanded version algorithm Alpha-beta was not successful because of insufficient computational resources. Algorithm Monte Carlo tree search, on the other hand, was more successful in expanded version.
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