Studying AI at BUT

Artificial intelligence is changing technical fields, research and practice. At BUT, you can encounter AI as a prospective student, student, doctoral student, teacher or researcher. This page is a guide to degree programmes, specialisations, courses, final theses and tools that will help you get started or deepen your knowledge of AI.

Main study path: Information technology and artificial intelligence

The most direct way to study AI at BUT is the follow-up master's program Information technology and artificial intelligence at the Faculty of Information Technology BUT. The program deepens knowledge of information technology, working with data, experimental evaluation, computing systems and methods used for the implementation of artificial intelligence.

Basic information about the program

Program name	Information technology and artificial intelligence
Faculty	BUT Faculty of Information Technology
Program Abbreviation	MITAI
Study type	follow-up master's program
Form of study	full-time study
Standard study time	2 years
Degree awarded	Ing.
Language of instruction	Czech
Program detail	Information technology and artificial intelligence - program detail

What the Programme Offers

• a deeper understanding of information technology, algorithms, data and modern computing systems,
• the ability to analyze problems and propose technical solutions using current AI methods,
• practical skills for implementing and evaluating models, systems and experiments,
• the possibility to professionally profile yourself using specializations according to the chosen direction,
• preparation for work in industry, research or continuation of doctoral studies.

AI-related Specialisations

In the Information Technology and Artificial Intelligence program, it is possible to profile in a number of specializations. Specializations that are particularly close to AI or support its technical use are selected below.

Selected specializations with a link to AI

Specialization	Typical topics	Who is it suitable for?
Machine learning	machine learning models, neural networks, working with data, evaluating models	for students who want to design, train and evaluate AI models
Computer vision	image analysis, object detection, visual data, industrial and multimedia applications	for those interested in AI in imaging, robotics, quality control and multimedia
Sound, speech and natural language processing	speech technologies, language models, text processing, audio data	for those interested in voice assistants, NLP, conversational systems and multimodal AI
Intelligent systems	decision making, planning, knowledge systems, autonomous and adaptive systems	for students aiming to design intelligent applications and systems
Smart device	sensors, embedded systems, smart devices, hardware-oriented AI applications	for those interested in connecting AI with equipment, measurement and technical practice
Bioinformatics and biocomputing	computational methods in biology, data analysis, modeling of biological systems	for those interested in AI in healthcare, biology and data-oriented research
Cyberphysical systems	interconnection of software, devices, sensors and control in technical systems	for students who want to use AI in industry, automation and smart devices
Cyber security	system security, threat detection, data protection, secure deployment of intelligent applications	for those interested in AI security, data security and resilience of digital services
Supercomputing	powerful calculations, parallel processing, infrastructure for demanding experiments	for those interested in large models, extensive data and computationally demanding AI tasks

AI across BUT

AI is not just a subject of computer science. At the technical university, it is applied in a number of areas: working with data, simulations, construction design, robotics, automation, communication technologies, chemistry, materials research, architecture, design and economic decision-making. This section can serve as a guide for students who want to connect AI to their field.

Examples of fields where AI can be developed at BUT

Direction	Examples of the use of AI	Where to look next
Informatics and software technologies	machine learning, NLP, computer vision, AI assistants, data systems, cyber security	FIT BUT, study programs, subjects and research groups focused on AI and data
Electrical engineering, communication and cybernetics	signal, speech and image processing, intelligent sensors, measurement, robotics, control and automation	FEEC BUT and programs focusing on electronics, communication, cybernetics and measurement
Engineering, production and industry, energy	predictive maintenance, production optimization, digital twins, robotics, evaluation of experiments	FME BUT, applied informatics, management, robotics and industrial applications of data analytics
Construction, infrastructure and transport	construction optimization, sensor data analysis, risk modeling, prediction and planning	FCE BUT and topics focused on modeling, construction, transportation and data decision support
Chemistry, materials and bioengineering	prediction of material properties, evaluation of laboratory data, bioinformatics, process modeling	FCH BUT, CEITEC BUT and research facilities working with experimental and data methods
Architecture, art and design	generative design, visualization, image work, creative prototyping, critical and ethical use of AI	FA BUT, FFA BUT and interdisciplinary projects connecting technology, creation and social impact
Business, management and services	process analysis, decision support, automation of administration, work with data and customer scenarios	FBM BUT, lifelong learning and courses focused on the practical use of AI in organizations

Recommended study routes

I want to develop AI models and applications

Start with a solid foundation in computer science, programming, math, and working with data. In your follow-up studies, focus on machine learning, computer vision, natural language processing, intelligent systems or supercomputing.

I want to use AI in a technical or industrial field

Combine your major with data analytics, programming, simulations, and machine learning fundamentals. Suitable topics are predictive maintenance, design optimization, measurement, sensor data, automation and decision support.

I want to use AI in research

Connect domain expertise with methods for data analysis, statistics, modeling and reproducible experiments. Also focus on safe work with data, documentation of the use of AI tools and verification of results.

I want to use AI in studying, writing and presenting

AI can help with explaining concepts, creating an outline, proofreading language, preparing presentations or designing a study plan. For student and final papers, always respect the instructor's instructions and be transparent about how AI was used.

I want to connect AI with creative practice

Focus on generative tools, working with image, text, sound and multimodal creation. Knowledge of the ethical, copyright and social impacts of AI should also be part of this journey.

Courses, training and self-study

In addition to accredited study programs, you can also use trainings, webinars, tutorials and courses for different advanced levels at BUT. The offer is constantly changing according to the current needs of the university.

• AI courses and training at BUT – current and past events, online courses and webinars.
• Instructions for students – practical scenarios of using AI in learning, writing, programming and working with data.
• Instructions for teachers – recommendations for integrating AI into teaching and assessment.
• AI tools at BUT – an overview of supported, commercial and open-source tools and e-infrastructure links.
• BUT's position on the use of AI – basic rules and recommendations for education, creative activity and administration.

Student projects and final theses

AI is a suitable topic for semester projects, bachelor's and master's theses and doctoral studies. The topic can be theoretical, implementation, experimental or application. The best topics often arise from connecting the student's field to a specific data or technical problem.

Examples of suitable topics

• analysis and classification of image, sound, text or time series,
• the use of large language models in teaching, research or administration,
• AI assistants for working with documents, knowledge bases or laboratory data,
• predictive models in industry, transport, energy, construction or chemistry,
• generative design, visualization, simulation and prototyping,
• security, ethics, transparency and reliability of AI systems.

How to choose a topic

1. Be clear about whether you want to develop AI or use it as a tool in your field.
2. Find a leader or workplace that deals with a related topic.
3. Verify that you have data, computing resources, and expert consultation available.
4. Agree on rules for using AI tools, especially for non-public, personal or sensitive data.
5. In your work, transparently describe which AI tools were used and for what purpose.

Rules for safe and fair use of AI

BUT understands generative AI as an assistive and consultative tool. When studying, it is therefore important to follow the rules of a specific subject or work, protect data and not use AI in a way that replaces one's own independent performance where independence is valued.

Basic principles

• Always consult the guidelines of the specific subject, supervisor or faculty when using AI.
• Critically verify AI outputs and do not rely on them without checking sources and facts.
• Do not submit personal, sensitive, confidential, internal or contractually protected data to public AI tools.
• Respect the copyright and license terms of the tools for text, code, data, images and other output.
• In seminar, project and final papers, transparently describe how the AI tool was used.

More detailed information can be found in the section Using generative AI at BUT and in the instructions for students.

How to start

1. Choose your level. Applicants should start with an overview of study programs, current students with a selection of subjects, specializations and project topics.
2. Check the rules. Before using AI in your study work, read the instructions of the BUT, the faculty and the specific teacher.
3. Try the basic course. Start with a course on prompting, AI in teaching or working with data according to your role.
4. Find industry continuity. AI is most valuable where it solves a specific problem in your industry.
5. Work securely with data. Do not put sensitive, non-public or personal data into public tools without a clear legal and security assessment.
6. Verify the results. Take AI output as suggestion, not authority. The author of the thesis is responsible for the correctness of the output.

Frequently Asked Questions

Can I use AI while studying?

Yes, if the use is in accordance with the rules of BUT, the faculty and the specific subject. AI is intended to serve as an assistant for learning, consulting, checking, explaining or supporting creation. It may not replace the actual fulfillment of the study obligation.

Do I have to mention the use of AI in the thesis?

For final and other evaluated works, it is advisable to transparently indicate whether AI was used, to what extent and for what purpose. The specific form may vary according to the requirements of the faculty, supervisor or teacher.

Where do I find out which AI tools I can use?

Start with a page AI tools at BUT. For sensitive, personal, non-public or contractually protected data, always verify that the chosen tool is appropriate and secure.

Is this page a complete list of all AI activities at BUT?

No. The site is a signpost. AI is being developed at BUT across faculties, research teams, projects and subjects. You can also find up-to-date information in the catalog of study programs, in the course menu, on the faculty websites and in the news of the AI portal.

Useful links

• Information technology and artificial intelligence - program detail
• Study programs at BUT
• Subjects at BUT
• Final theses
• Doctoral studies
• AI courses and training at BUT
• AI tools at BUT
• AI tutorials and recommendations
• Contacts for AI issues
• BUT's position on the use of AI

Responsible person: doc. Ing. Petr Koňas, Ph.D.