====== Explainable Artificial Intelligence ======
  * Course ID: WFAIS.IF-XG325.0
  * Room: G-1-05
  * Teachers:
    * Lectures: [[https://szymon.bobek.re|dr inż. Szymon Bobek]] (SBK)
    * Labs:  [[https://szymon.bobek.re|dr inż. Szymon Bobek]] (SBK)

====== Lectures ======
  * **[07.10.2024]** [SBK] {{ :courses:xai:1_introduction_to_explainable_artificial_intelligence_2024.pdf |Introduction to xAI}}
  * **[14.10.2024]** [SBK] {{ :courses:xai:2_inherently_interpretable_models.pdf |Inherently interpretable models I}}
  * **[21.10.2024]** [SBK] {{ :courses:xai:3_inherently_interpretable_models.pdf |Inherently interpretable models II}}
  * **[28.10.2024]** [SBK] {{ :courses:xai:4_surrogate_models.pdf |Global model-agnostic explanations and surrogate models}}
  * **[4.11.2024]** [SBK] {{ :courses:xai:5_local_model-agnostic_explanations.pdf |Local model-agnostic explanations I}}
  * **[18.11.2024]** [SBK] {{ :courses:xai:6_local_model-agnostic_explanations.pdf |Local model-agnostic explanations II}}
  * **[25.11.2024]** [SBK] Hands-on programming assignments
  * **[2.12.2024]** [SBK] {{ :courses:xai:7_conterfactual_and_adversarial_explanations.pdf |Counterfactual explanations}}
  * **[9.12.2024]** [SBK] {{ :courses:xai:8_evaluation_methods.pdf |Evaluation of XAI algorithms}}
  * **[16.12.2024]** [SBK] {{ :courses:xai:9_explainability_in_neural_networks.pdf |Explanations in Neural Networks}}
  * **[13.01.2025]** [SBK] {{ :courses:xai:10_industrial_challenges_for_xai.pdf |Challenges of XAI in Industrial applications}}
  * **[20.01.2025]** [SBK] Hands-on programming assignments


====== Labs ======
  * **[07.10.2023]** [SBK] [[.:lab1|Introduction to working environment, basics of data manipulation and visualization and bias identification]]
  * **[14.10.2023]** [SBK] [[.:lab2|Inherently interpretable models]]
  * **[21.10.2023]** [SBK] [[.:lab3|Inherently interpretable models II]]
  * **[28.10.2023]** [SBK] [[.:lab4|Global model-agnostic approaches I]]
  * **[04.11.2023]** [SBK] [[.:lab5|Local model-agnostic approaches I]]
  * **[18.11.2023]** [SBK] [[.:lab6|Local model-agnostic approaches II]]
  * **[25.11.2023]** Test I
  * **[25.11.2023]** [SBK] [[.:p1|Programming Assignment I]]
  * **[02.12.2023]** [SBK] [[.:lab7|Counterfactual explanations]]
  * **[09.12.2023]** [MTM] [[.:lab8|Evaluation of XAI methods]]
  * **[16.12.2024]** [BMK] [[.:lab9|Explainability in DNN]]
  * **[13.01.2024]** [SBK] [[.:p2|Programming Assignment II]]
  * **[20.01.2024]** Test II
====== Grading rules ======
  * 100 EXP is 100% of the total points (MAX) from the lab. This consists of:
    * 2x25 EXP - two tests, covering material from the laboratories and lecture
    * 2x25 EXP - two assignments carried out in groups (2-5 people) during the laboratory

  * 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.
    * 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 60% of points in all tests.
    * 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
====== References ======
  * [[https://christophm.github.io/interpretable-ml-book/|Interpretable Machine Learning]]