Explainable Artificial Intelligence

Course ID: WFAIS.IF-XG325.0
Room: G-1-08
Teachers:
- Lectures: dr inż. Szymon Bobek (SBK)
- Labs: dr inż. Szymon Bobek (SBK)

Lectures

[12.10.2026] [SBK] Introduction to working environment, basics of data manipulation and visualization and bias identification
[19.10.2026] [SBK] Inherently interpretable models
[26.10.2026] [SBK] Inherently interpretable models II
[09.11.2026] [SBK] Global model-agnostic approaches I
[16.11.2026] [SBK] Local model-agnostic approaches I
[23.11.2026] [SBK] Local model-agnostic approaches II
[30.11.2026] Test I
[30.11.2026] [SBK] Programming Assignment -- start
[07.12.2026] [SBK] Counterfactual explanations
[14.12.2026] [MTM] Evaluation of XAI methods
[21.12.2026] [BMK] Explainability in DNN
[11.01.2027] [SBK] Programming Assignment -- end
[18.01.2027] Test II
[18.01.2027] [SBK] Projects presentations/discussion

100 EXP is 100% of the total points (MAX) from the lab. This consists of:
- 2×25 EXP - two tests, covering material from the laboratories and lecture
- 50 EXP - mini project assignment
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 laboratory projects must be submitted on time
- You must obtain at least 60% of points in all tests. In total, meaning (T1_score+T2_score)/(50) should be greater or equal to 0.6).
- Two unexcused absences are allowed.
- Each subsequent absence results in a deduction of 10 EXP.
- Min 50% of presence at laboratories is required
You need to pass the laboratories to take the exam.