Length of Study: 2 years
Min. Credits: 120
Degree Programme: Information Technology and Artificial Intelligence
Language of Instruction: Czech
Form of Study: full-time
Accredited from: 2019 Accredited till: 2029
The goal of the study is to make students familiar with theories, technologies and procedures being used in intelligent systems development, and to teach them to develop such systems by applying advanced development tools, methods and technologies. The offer of optional courses together with the choice of the theme of the individual technical project and the Master Thesis allow students to individually select their specialization in various theoretical and application areas. The graduates find employment in research, development and design of all kinds of intelligent systems. Due to a very good theoretical education and a wide universal basis of the specialization, a high adaptability of the graduates to all actual demands of their future professional practice in intelligent system and also in other areas of information technology is ensured.
The resulting knowledge and skills of the graduate and the possibilities of its application:
- The graduate has a general knowledge of the intelligent systems theory and skills to design, implement and apply intelligent systems especially in the areas of computer vision, natural language processing, data mining, intelligent sensors and control systems.
- The graduate is ready for research, development and design of various intelligent systems starting from simple embedded systems in domestic machines to very complex systems of autonomous mobile robots.
- Graduate find employment especially in research and design divisions and operation sites of various companies and institutions involved in the development of systems with embedded intelligent subsystems, and in the army, education, health institutes, and practically in any industrial enterprise.
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
Choose academic year and curriculum
|AGS||Agents and Multiagent Systems||5||C||Ex||FIT|
|MSP||Statistics and Probability||6||C||Cr+Ex||FME|
|TIN||Theoretical Computer Science||7||C||Cr+Ex||FIT|
|FLP||Functional and Logic Programming||5||C||Cr+Ex||FIT|
|PRL||Parallel and Distributed Algorithms||5||C||Cr+Ex||FIT|
|PP1||Project Practice 1||5||E||Cr||FIT|
|PP2||Project Practice 2||5||E||ClCr||FIT|
Duty: C - compulsory, CEx - compulsory-elective group x, R - recommended, E - elective