F3111 AI: the new hope for scientific programming?

Faculty of Science
Autumn 2025
Extent and Intensity
0/2/0. 2 credit(s). Type of Completion: z (credit).
In-person direct teaching
Teacher(s)
Dr. rer. nat. Tereza Jeřábková (seminar tutor)
Mgr. Petr Zikán, Ph.D. (seminar tutor)
Henry James Best V, Ph.D. (assistant)
Julia Katharina Lamprecht, M.Sc. (assistant)
Guaranteed by
Dr. rer. nat. Tereza Jeřábková
Department of Theoretical Physics and Astrophysics – Physics Section – Faculty of Science
Contact Person: Dr. rer. nat. Tereza Jeřábková
Supplier department: Department of Theoretical Physics and Astrophysics – Physics Section – Faculty of Science
Timetable
Tue 10:00–11:50 Fcom,01034, Tue 10:00–11:50 Fcom,01034
Prerequisites
Basic knowledge of programming (e.g., in Python) – familiarity at the level of undergraduate courses is sufficient.
Course Enrolment Limitations
The course is offered to students of any study field.
The capacity limit for the course is 50 student(s).
Current registration and enrolment status: enrolled: 14/50, only registered: 0/50, only registered with preference (fields directly associated with the programme): 0/50
Course objectives
The goal of the course is to introduce students to new trends and methods in artificial intelligence (AI) and their practical use in programming tasks in physics and astrophysics. Teaching is based on practical work on an independent project using professional tools (including access to paid platforms). The main development environment will be Visual Studio Code and Github.
Learning outcomes
• Students will gain an overview of current AI tools applicable in programming and data analysis in physics and astrophysics. • They will be able to use tools such as the OpenAI platform, GitHub Copilot, and simple AI agents safely and effectively. • They will acquire practical experience in designing, implementing, and presenting a programming project using AI and code management systems.
Syllabus
Introduction to AI tools: history, overview, ethics, and safety Working with OpenAI tools and agents (including a basic introduction to GitHub Copilot and the Codex concept) Working with Visual Studio Code and GitHub (repositories, collaboration, workflow) Integrating AI into scientific programming: tasks, tests, simple examples Project proposal and planning Ongoing consultations, testing, troubleshooting Project presentations and reflection (Note: The syllabus may be adjusted based on the number of enrolled students and current developments in AI technologies.)
Literature
    recommended literature
  • Master ChatGPT and OpenAI APIs By Building AI Tools in Python: Crafting Intelligent Python Applications with ChatGPT and OpenAI APIs (Paulo Dichone, 2024, ISBN 9781835885628)
    not specified
  • Articles on the application of AI in science (according to current recommendations by instructors)
Teaching methods
Lectures, demonstrations, practical hands-on exercises in the form of project-based work. Note: The course structure is flexible and reflects current developments in AI. Tools used during the semester may change depending on availability and relevance.
Assessment methods
Ongoing evaluation of semester project work, active participation, and submission of the final project.
Náhradní absolvování
Please contact the course coordinator in advance to discuss individual options. We will do our best to accommodate specific situations; however, due to the hands-on nature of the course, finding a solution may not be feasible.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
  • Enrolment Statistics (recent)
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