====== Human-Centered Artificial Intelligence ======
  * Course ID: WFAIS.IF-F211.0
  * Teachers:
    * Lectures: [[https://gjn.re|prof. dr hab. inż. Grzegorz J. Nalepa]] (GJN)
    * Labs: 
      * [[https://krzysztof.kutt.pl|dr inż. Krzysztof Kutt]] (KKT)
      * [[https://www.usosweb.uj.edu.pl/kontroler.php?_action=katalog2/osoby/pokazOsobe&os_id=334478|dr Luiz do Valle Miranda]] (LVM)

===== Lectures =====
  - **05.10.2026** History, backwards
  - **12.10.2026** Agents
  - **19.10.2026** Search
  - **26.10.2026** Uncertainty
  - **02.11.2026** Bayes Nets
  - **09.11.2026** Knowledge representation and reasoning
  - **16.11.2026** ML 101
  - **23.11.2026** CSP
  - **30.11.2026** Reasoning systems
  - **07.12.2026** MDP and RL
  - **14.12.2026** 
  - **21.12.2026** 
  - **11.01.2027** 
  - **18.01.2027** 
  - **25.01.2027** 

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

  - **[06.10.2026]** [KKT] [[.:lab_intro|Intro]]
  - **[13.10.2026]** [KKT] [[.:lab_agents|Agents]]
  - **[20.10.2026]** [KKT] [[.:lab_search|Search]]
  - **[27.10.2026]** [KKT] [[.:projects|Miniprojects - intro]]
  - **[03.11.2026]** [LVM] [[.:lab_advsearch|Adversarial Search]]
  - **[10.11.2026]** [LVM] [[.:lab_uncertainty|Uncertainty]]
  - **[17.11.2026]** [LVM] [[.:lab_krr|Knowledge Representation]]
  - **[24.11.2026]** [LVM] FIXME [[.:lab_ml|Learning, Supervised and Unsupervised]]
  - **[01.12.2026]** [KKT] [[.:projects|Miniprojects - checkpoint]]
  - **[08.12.2026]** [???] FIXME [[.:lab_csp|Constraint satisfaction problems]]
  - **[15.12.2026]** [???] FIXME [[.:lab_fixme|Automated reasoning systems]]
  - **[22.12.2026]** [???] FIXME [[.:lab_rl|Learning, Reinforcement]]
  - **[12.01.2027]** [???] FIXME [[.:lab_rec|Recommendation systems]]
  - **[19.01.2027]** [???] Written test :!:
  - **[26.01.2027]** [KKT] Project presentation and evaluation
===== Grading rules =====
  * 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
    * 25 <del>20</del> EXP - miniproject, done in groups of 2-4
    * 25 <del>30</del> 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 lab within the basic deadline (50% of the MAX as listed above).
  * 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

===== Learn more! =====

Additional materials to AIMA handbook:
  * [[https://aimacode.github.io/aima-exercises/|AIMA Exercises]] -- exercises grouped by book's chapters
  * [[https://github.com/aimacode/aima-python/|AIMA-Python]] -- official repo with Python implementations

Related courses:
  * [[https://stanford-cs221.github.io/spring2026/|CS221: Artificial Intelligence: Principles and Techniques]] @ Stanford
  * [[https://inst.eecs.berkeley.edu/~cs188/su26/|CS 188]] @ Berkeley