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===== Knowledge in AI Systems (KnAIS) =====

  * Course: Knowledge in AI Systems (WFAIS.IF-XG327.0)
  * Teachers:
    * [[https://gjn.re/|prof. dr hab. inż. Grzegorz J. Nalepa]] (GJN) -- lecture
    * [[https://krzysztof.kutt.pl/|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] {{ :courses:knais:knowledge_graphs_101.pdf |Knowledge graphs 101}}
  - **28.10.2024** [GJN] Reasoning with knowledge
  - **04.11.2024** [KKT] {{ :courses:knais:knowledge_graphs_201.pdf |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] {{ :courses:knais:where_is_the_knowledge.pdf |Where is the knowledge}}
  - **13.01.2025** [GJN] Explanations and knowledge
  - **20.01.2025** [KKT] {{ :courses:knais:xai_with_knowledge.pdf |Insightful explanations with knowledge}}
  - **27.01.2025** [GJN] Summary and outlook


==== Labs ====
^  No.  ^  Date        ^ Topic                                                   ^ Materials ^
|   1.  |  02.10.2024  | Modelling smart home entities                           | [[https://colab.research.google.com/drive/1lJCHt2g0T4oryXPpm9vUsA35xq0mWi12?usp=sharing|Lab 1]] |
|   2.  |  09.10.2024  | Graphs: RDF and the Semantic Web                        | [[https://colab.research.google.com/drive/1x2TzWFFJZn7Mogji22kWYf_agoMAbiB7?usp=drive_link|Lab 2]] |
|   3.  |  16.10.2024  | Graphs: ontologies and SPARQL                           | [[https://colab.research.google.com/drive/1Yq4DiRjBZwuh5hvUSz7chJj23LaXcsAQ?usp=drive_link|Lab 3]] and [[https://colab.research.google.com/drive/1cA27nJFlPVWrAO-QzlkV9oWAwL_D2Jt0#scrollTo=UjRJJVTwN5uQ|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        | [[https://colab.research.google.com/drive/1uATXydLuPtVMX8Cp-FnLKQLRm7VKI-N3?usp=sharing|Lab 4]] |
|   6.  |  06.11.2024  | Property Graphs and Cypher                           | [[https://colab.research.google.com/drive/1tRxVWE7xmCANHgADxx_cHZpkkBgvGFR6?usp=sharing|Lab 5]]|
|   7.  |  13.11.2024  | Colloquium 1       | [[https://docs.google.com/document/d/1Gdr1GIMP9lGyUWM1vAj3Hr6FlBIdXY5RSbt2EI_GLp4/edit?tab=t.0|Colloquium 1]]|
|   8.  |  20.11.2024  | Machine Learning 101                       | [[https://colab.research.google.com/drive/18zAOHDSDhSAKQA2HV74bPPCQ9wq3kE9z?usp=sharing|Lab 6]]|
|   9.  |  27.11.2024  | Graph Neural Networks                                   | [[https://colab.research.google.com/drive/1E9jIuA9swf2OVKSl3xDe1xeqRP8CuRx8?usp=sharing|Lab 7]]|
|  10.  |  04.12.2024  | eXplainable AI                                          | [[https://colab.research.google.com/drive/1BR3GB3vfv9tjXjg6hxjueZa39ScnXzZp?usp=sharing|Lab 8]]|
|  11.  |  11.12.2024  | Logic: Prolog                                           | [[https://colab.research.google.com/drive/1uuS7IfgLOtcDJP6ODa2V7pfVjeevzRhE?usp=sharing|Lab 9]]|
|  12.  |  18.12.2024  | Cancelled                               | Cancelled|
|  13.  |  08.01.2025  | [ONLINE] Projects consultation and Neurosymbolic AI: ProbLog, DeepProbLog | [[https://colab.research.google.com/drive/17-vhQBgSMHHP420z5rs0A4kYmosvf_rC?usp=sharing|Lab 10 - Problog]] and [[https://colab.research.google.com/drive/17m35kBbgOXCSwxFbYW-h_6CokuaRHjOh?usp=sharing|Lab 11 - DeepProblog]] |
|  14.  |  15.01.2025  | [ONLINE] Projects presentation                                   | Projects presentation                                   |
|  16.  |  22.01.2025  | Colloquium 2                                            | [[https://docs.google.com/document/d/113Z0bhGB1pmeMtkIE0J0sXLhX1iKqKUNKI-UnW43q7Q/edit?usp=sharing|Colloquium 2]] |

==== Grading rules ====

  * Lab:
    * **100 EXP** = 100% (MAX). You can earn points for:
      * 2x**50 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! ====

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