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)

1. 05.10.2026 History, backwards
2. 12.10.2026 Agents
3. 19.10.2026 Search
4. 26.10.2026 Uncertainty
5. 02.11.2026 Bayes Nets
6. 09.11.2026 Knowledge representation and reasoning
7. 16.11.2026 ML 101
8. 23.11.2026 CSP
9. 30.11.2026 Reasoning systems
10. 07.12.2026 MDP and RL
11. 14.12.2026
12. 21.12.2026
13. 11.01.2027
14. 18.01.2027
15. 25.01.2027

Note: Required reading for labs: AIMA = Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 4th ed, Pearson, 2021

1. [06.10.2026] [KKT] Intro
2. [13.10.2026] [KKT] Agents
3. [20.10.2026] [KKT] Search
4. [27.10.2026] [KKT] Miniprojects - intro
5. [03.11.2026] [LVM] Uncertainty
6. [10.11.2026] [LVM] Knowledge representation basics
7. [17.11.2026] [LVM] Learning, Supervised
8. [24.11.2026] [LVM] Learning, Unsupervised
9. [01.12.2026] [KKT] Miniprojects - checkpoint
10. [08.12.2026] [???] Constraint satisfaction problems
11. [15.12.2026] [???] Automated reasoning systems
12. [22.12.2026] [???] Learning, Reinforcement
13. [12.01.2027] [???] Recommendation systems
14. [19.01.2027] [???] Written test
15. [26.01.2027] [KKT] Project presentation and evaluation

• 100 EXP is 100% of the total points (MAX) from the lab. This consists of:
  • 30 EXP = 10x 3 EXP - entrance tests for all non-project labs
  • 20 EXP = 10x 2 EXP - in-lab checkpoints for all non-project labs
  • 20 EXP - miniproject, done in groups of 2-4
  • 30 EXP - written test, covering material from the laboratories and lecture

• The above result may be increased by any “pluses” for activity during classes and challenges (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

Additional materials to AIMA handbook:

• AIMA Exercises – exercises grouped by book's chapters
• AIMA-Python – official repo with Python implementations

Related courses:

• CS221: Artificial Intelligence: Principles and Techniques @ Stanford
• CS 188 @ Berkeley