Human-Centered Artificial Intelligence
- Course ID: WFAIS.IF-F211.0
- Teachers:
- Lectures: prof. dr hab. inż. Grzegorz J. Nalepa (GJN)
- Labs:
- dr inż. Krzysztof Kutt (KKT)
- dr Luiz do Valle Miranda (LVM)
Lectures
- 05.10.2026
- 12.10.2026
- 19.10.2026
- 26.10.2026
- 09.11.2026
- 16.11.2026
- 23.11.2026
- 30.11.2026
- 07.12.2026
- 14.12.2026
- 21.12.2026
- 11.01.2027
- 18.01.2027
- 25.01.2027
Labs
- [06.10.2026] [KKT] Search
- [13.10.2026] [KKT] Agents
- [20.10.2026] [KKT] Uncertainty
- [27.10.2026] [KKT] Miniprojects - intro
- [03.11.2026] [LVM] Knowledge representation basics
- [10.11.2026] [LVM] Learning, Supervised
- [17.11.2026] [LVM] Learning, Unsupervised
- [24.11.2026] [???] Miniprojects - checkpoint
- [01.12.2026] [???] Constraint satisfaction problems
- [08.12.2026] [???] Automated reasoning systems
- [15.12.2026] [???] Learning, Reinforcement
- [22.12.2026] [???] Recommendation systems
- [12.01.2027] [???] Miniprojects - nailing
- [19.01.2027] [???] Written test
- [26.01.2027] [???] Project presentation and evaluation
Grading rules
- 100 EXP is 100% of the total points (MAX) from the lab. This consists of:
- 27 EXP = 9x 3 EXP - entrance tests for all non-project labs (except the 1st one)
- 20 EXP = 10x 2 EXP - in-lab checkpoints for all non-project labs
- 23 EXP - miniproject, done in pairs
- 30 EXP - written test, covering material from the laboratories and lecture
- The above result may be increased by any “pluses” for activity during classes (1 plus = 1 EXP)
- Advantages are taken into account only when passing the exam within the basic deadline.
- All miniprojects must be submitted on time (final lab). The mark for late projects will be multiplied by 0.5 (i.e. a maximum of half the number of points can be obtained for a late project).
- You must obtain at least 50% of points in the test.
- Two unexcused absences are allowed.
- Each subsequent absence results in a deduction of 10 EXP.
- Grading scale:
- >= 90 EXP – bdb
- >= 80 EXP – db+
- >= 70 EXP – db
- >= 60 EXP – dst+
- >= 50 EXP – dst
- < 50 EXP – ndst