====== B4B36ZUI / BE4B36ZUI -- Introduction to Artificial Intelligence ======


The goal of this subject is to introduce the basics of artificial intelligence. We will cover the algorithms of informed and uninformed state space, problem solving methods, reinforcement learning, knowledge representation and (sequential) decision making under uncertainty. We will avoid most of classical machine learning, since that is the focus of other courses.


===== Course =====



**[[lectures|Lectures]]:** 

[[http://cs.felk.cvut.cz/en/people/lisyvili|Viliam Lisý]] [[http://cs.felk.cvut.cz/en/people/lisyvili|{{http://cs.felk.cvut.cz/upload/persons/d293a37ac14639b6abe01c39c2b993af326b0479.jpg?50}}]],
 [[http://cs.felk.cvut.cz/en/people/bosanbra|Branislav Bošanský]] [[http://cs.felk.cvut.cz/en/people/bosanbra|{{http://cs.felk.cvut.cz/upload/persons/606b5f296e6078827767959f34300998eccac573.jpg?50}}]]



**[[seminars|Seminars]]:** 

[[http://cs.felk.cvut.cz/en/people/tomaspe7|Petr Tomášek]] [[http://cs.felk.cvut.cz/en/people/tomaspe7|{{http://cs.felk.cvut.cz/upload/persons/2ea3fccc340e6e616fee08ab55abe56a65ab3e5a.jpg?50}}]],
[[https://cs.felk.cvut.cz/en/people/kubicon3|Ondřej Kubíček]]
[[https://cs.felk.cvut.cz/en/people/kubicon3|{{https://cs.fel.cvut.cz/upload/persons/48fef35a6f92b5fcbda4349fd2e19506c5d4e391.jpg?50}}]],
[[mailto:drabeka1@fel.cvut.cz|Karolina Drabent]]
[[https://cs.fel.cvut.cz/en/people/drabeka1|{{:courses:zui:karolina2.jpg?58}}]],
[[https://cs.fel.cvut.cz/en/people/mrkosja1|Jan Mrkos]]
[[https://cs.fel.cvut.cz/en/people/mrkosja1|{{https://cs.fel.cvut.cz/upload/persons/5916775f8ae2448362c692d079b31c676f044d86.jpg?50}}]], 
[[https://cs.felk.cvut.cz/en/people/dangxuzh|Xuzhe Dang]] [[https://cs.felk.cvut.cz/en/people/dangxuzh|{{https://cs.felk.cvut.cz/upload/persons/bfa72d165698b09312dc7efbeab66a4c981f22ad.jpg?50}}]]



**Time tables:** [[https://intranet.fel.cvut.cz/cz/education/rozvrhy-ng.B242/public/html/predmety/47/02/p4702906.html|Czech]] [[https://intranet.fel.cvut.cz/cz/education/rozvrhy-ng.B242/public/html/predmety/51/48/p5148406.html|English]]

[[https://cw.felk.cvut.cz/brute|BRUTE - homework submissions]]

[[https://drive.google.com/drive/folders/1UJfamwjIXG8xXCi5cj_hd0jiRX1yEjW5|Labs Notebooks (Google drive, use FEL Google Account)]]



====Homework Assignments====

The students can gain at most **30 points** for homework assignments. In order to get the credit (zápočet), they have to submit each task for at least 5 points (before the penalisation for late submissions - penalisation does not prevent getting the credit) and gain at least **15 points** for home works overall.

Penalisation for late submission:
  * less than 24h after the deadline -- losing 20% points
  * more than 24h after the deadline -- losing 100% points 

^Task ^ Deadline ^ Points ^ Minimal points ^
|Task 1: Path planning (A*)  | 25.3. | 10 | 5 |
|Task 2: Reinforcement Learning | 22.4. | 10 | 5 |
|Midterm Test | 14-15.4.2025 | 15 | 0 |
|Task 3: Playing a two-player game | 20.5. | 10 | 5 |


**Always work on your assignments individually. Plagiarism is being detected and it is not tolerated.** If you have an objective reason for difficulties with finishing the assignment on time, contact us ASAP, please.

====Midterm Test====

In the middle of the semester, there will be a test similar to the final exam for **15 points**.

===== Successfully passing the subject and the final evaluation =====
  * Getting the credit (zápočet)
  * Passing the final exam
  * The sum of the points for the final exam, midterm exam, and home works determines the final grade (50-59p. = E, ..., 90-100p. = A).
==== Final Exam ====

The final exam is for up to 55 points:
  * the exam is in written form
  * the students must gain at least 28 points to pass the exam
  * the students with an overall sum of scores over 80 for the whole subject will have to pass also brief oral examination to defend the grade
  * the topics of the questions for the exam are given by the slides, however, the slides are **not meant to be the primary study materials** and are not self-explanatory
  * you can use a calculator during the exam (not a mobile phone, no other materials)
  * the exam lasts 3 hours


Questions will be similar to the midterm test shown below, but they will cover all the content covered in the lectures and labs.

Exam dates:
  * Mo 26.5. 13:00-17:00
  * Fr 6.6.   9:00-13:00
  * Th 26.6. 13:00-17:00
  * ?


==== Midterm Test ====
Example of the questions for midterm test:
    * {{ :courses:zui:zui_example_questions.pdf |}}
    * {{ :courses:zui:zui_example_questions_2.pdf |}}


===== Literature =====
  * [AIMA] Russel, S. a Norvig, P.: Artificial Intelligence: A Modern Approach (2nd edition), Prentice Hall, 2003
      * příslušné kapitoly k dispozici na vyžádání 
  * [RLbook] Richard S. Sutton and Andrew G. Barto: Reinforcement Learning: An Introduction [[http://incompleteideas.net/book/the-book.html]]
  * {{http://cs.ucla.edu/~rosen/161/notes/alphabeta.html|Ilustrace alpha-beta}}

More details on chapters and links to other resources are in the slides.