Intelligent Systems

Students will understand all the layers of the computer based systems including hardware (semiconductor components, logic networks, processors, peripheral devices), software (control and data structures, object orientation, programming languages, compilers, operating systems, databases), as well as their common applications (information systems, computer networks, artificial intelligence, computer graphics and multimedia). They will understand foundations of computer science (discrete mathematics, formal languages and their models, spectral analysis of signals, modelling and simulation). Graduates will be able to analyse, design, implement, test, and maintain common computer applications. They will be able to work efficiently in teams.

State Exam in Information Technology and Artificial Intelligence, specialization Intelligent Systems consists of the following parts:

• presentation and defense of master's thesis,
• oral exam, which combines the basic themes contained in the courses profiling the basis of Information Technology and Artificial Intelligence (Theoretical Computer Science, Statistics and Probability, Computer Systems Architectures, Artificial Intelligence and Machine Learning, Data Storage and Preparation, Functional and Logic Programming, Parallel and distributed algorithms, Modern trends in informatics),
• oral exam, which combines the basic themes contained in the courses profiling the basis of Intelligent Systems (Agent and Multiagent Systems, Intelligent Systems, Soft Computing, Knowledge Discovery in Databases, Simulation Tools and Techniques, Machine Learning and Recognition).

All parts of the state examination are held on the same date before the State Examination Board. The state exam can be taken by a student who has obtained the required number of credits in the prescribed composition necessary for the successful completion of the master's degree and has submitted the master's thesis in due time. The organization and course of the state examination are given by the corresponding internal standard of the faculty and by the relevant instructions of the program guarantor for state examinations.

• Detection and Classification of Road Users in Aerial Imagery Based on Deep Neural Networks
• User Interface for Registry Office Transcription
• Modelling for Genealogy
• Genetic Algorithms
• Visual Car-Detection on the Parking Lots Using Deep Neural Networks
• Computer Aided Recognition and Classification of Coat of Arms
• Word2vec Models with Added Context Information
• Reconfigurable ESP8266/ESP32-Based Node for IoT

Master's theses are stored at the FIT library, Božetěchova 2, Brno. The list of the master's theses, including the details is available at:
https://www-dev.fit.vutbr.cz/study/theses/