BECM36STAI – Selected Topics in AI

The course aims to immerse students in the forefront of AI research, covering current challenges, significant areas of study, and emerging trends in the field. The set of covered topics is updated regularly; in 2025/2026 the course is organised into four thematic blocks (see the schedule below). The course is a compulsory part of the prg.ai Master study plan.

Course parameters:

• Completion: Klasifikovaný zápočet (graded course credit)
• Credits: 6
• Extent: 2P+2C (28p+28c)
• Course guarantor: Tomáš Kroupa

Time and place:

• Lecture: Mon 11:00–12:30, room KN:E-327
• Tutorial: Mon 12:45–14:15, room KN:E-327

Blocks in 2025/2026:

• Explainable AI with Shapley value Tomáš Kroupa (TK) - 16 points
• Generative models (GANs, diffusion, applications) Jan Čech (JČ) - 28 points
• Planning for autonomous robots Tomáš Svoboda (TS), Martin Pecka (MP), Vojtěch Vonásek (VV) - 28 points
• SAT and SMT solving (computational logic, modern SAT/CDCL, SMT, applications) Martin Suda (MS) - 28 points

There are no formal prerequisites. The course is intended for master-level students with a solid background in mathematics and programming. Familiarity with basic machine learning and algorithms will be helpful.

The course is completed by a graded course credit.

• The main requirement is projects during the semester (the exact form, deliverables, and deadlines are announced by the lecturers for each thematic block).
• The final grade (A,…,F) will be determined by the sum of points obtained from all the parts of the course (<50 = F, 50-59 pts = E, …, 90-100 pts = A). The student can obtain 16 points from the first block and 28 points from each of the following blocks.
• The final grade reflects the quality of the submitted work and (where applicable) the presentation / discussion of the results.

Tutorials are used for guided discussion, exercises related to the lecture topic, and work on the semester projects.

Study materials will be announced for each block.