Knowledge in AI Systems (KnAIS)
- Course: Knowledge in AI Systems (WFAIS.IF-XG327.0)
- Teachers:
- prof. dr hab. inż. Grzegorz J. Nalepa (GJN) – lecture
- dr inż. Krzysztof Kutt (KKT) – lecture
- Luiz do Valle Miranda – laboratory classes
Lectures
- 07.10.2024 [GJN] Introduction
- 14.10.2024 [GJN] Knowledge and its representation
- 21.10.2024 [KKT] Knowledge graphs 101
- 28.10.2024 [GJN] Reasoning with knowledge
- 04.11.2024 [KKT] Knowledge graphs 201
- 18.11.2024 [GJN] Learning and knowlege
- 25.11.2024 [GJN] The knowledge soup - if we do not know all
- 02.12.2024 [GJN] Neurosymbolic systems and Problog
- 09.12.2024 [GJN] From Problog to DeepProblog
- 16.12.2024 [KKT] Where is the knowledge
- 13.01.2025 [GJN] Explanations and knowledge
- 20.01.2025 [KKT] Insightful explanations with knowledge
- 27.01.2025 [GJN] Summary and outlook
Labs
| No. | Date | Topic | Materials |
|---|---|---|---|
| 1. | 02.10.2024 | Modelling smart home entities | Lab 1 |
| 2. | 09.10.2024 | Graphs: RDF and the Semantic Web | Lab 2 |
| 3. | 16.10.2024 | Graphs: ontologies and SPARQL | Lab 3 and Lab 3 ontology only |
| 4. | 23.10.2024 | Graphs: ontologies and SPARQL (continued) | Same as Lab 3 |
| 5. | 30.10.2024 | Reasoner + Rules and constraints: SWRL and SHACL | Lab 4 |
| 6. | 06.11.2024 | Property Graphs and Cypher | Lab 5 |
| 7. | 13.11.2024 | Colloquium 1 | Colloquium 1 |
| 8. | 20.11.2024 | Machine Learning 101 | Lab 6 |
| 9. | 27.11.2024 | Graph Neural Networks | Lab 7 |
| 10. | 04.12.2024 | eXplainable AI | Lab 8 |
| 11. | 11.12.2024 | Logic: Prolog | Lab 9 |
| 12. | 18.12.2024 | Cancelled | Cancelled |
| 13. | 08.01.2025 | [ONLINE] Projects consultation and Neurosymbolic AI: ProbLog, DeepProbLog | Lab 10 - Problog and Lab 11 - DeepProblog |
| 14. | 15.01.2025 | [ONLINE] Projects presentation | Projects presentation |
| 16. | 22.01.2025 | Colloquium 2 | Colloquium 2 |
Grading rules
- Lab:
- 100 EXP = 100% (MAX). You can earn points for:
- 2x50 EXP - two colloquia
- Extra credit for optional projects (to be agreed with the lab teacher)
- Optional “pluses” for in-class activity (1 plus = 1 EXP)
- Two unexcused absences are allowed.
- Any additional absence will result in a subtraction of 10 EXP.
- Lecture:
- Exam
- Grading scale:
- >= 90 EXP – 5.0
- >= 80 EXP – 4.5
- >= 70 EXP – 4.0
- >= 60 EXP – 3.5
- >= 50 EXP – 3.0
- < 50 EXP – 2.0
Learn more!
