/** <WRAP round important>Work in progress!!!</WRAP> **/

====== AE3M33UI: Artificial Intelligence ======
The course provides theoretically deeper knowledge of artificial intelligence in the range required by the Robotics branch of study. It consists of several parts: selected topics in pattern recognition and machine learning, basics of multiagent systems theory, and artificial life. The course emphasizes the relation of theoretical foundations and applications.

  * Master study program Cybernetics and robotics, compulsory course of Robotics branch.
  * The course is finished with an **assessment** and an **exam**.
  * After completion of this course, students get **6 credits**.
  * This corresponds to 2 hours of lecture, 2 hours of excercises and **6 hours of home work each week**! 
  * The official course description: [[http://www.feld.cvut.cz/education/bk/predmety/12/54/p12547304.html|A3M33UI]], [[http://www.fel.cvut.cz/education/bk/predmety/12/81/p12816404.html|AE3M33UI]]
  * [[.lectures:start|Lectures]], [[.exercises:start|Exercises]], [[.tasks:start|Tasks]]
  * Schedule [[http://www.fel.cvut.cz/cz/education/rozvrhy-ng.B152/public/cz/predmety/12/54/p12547304.html|A3M33UI]], [[http://www.fel.cvut.cz/cz/education/rozvrhy-ng.B152/public/cz/predmety/12/81/p12816404.html|AE3M33UI]]
  * [[https://cw.felk.cvut.cz/forum/forum-1226.html|Discussion forum]]
  * [[https://cw.felk.cvut.cz/ulohy/|Upload system]]


===== Assessment requirements =====

   * We will tolerate **at most 2 absences**.
   * In both parts of the semester (machine learning, multiagent systems), students can gain at most 20 points, i.e. 40 points in total.       
       * Each part will contain 1 or more evaluated assignments (homeworks); students must hand in their solutions (report and/or program code).
       * **Late policy:** late solutions will be penalized by 4 points for each started week of delay.
   * Students must get **at least 20 points**, i.e. **at least 10 points from each part**.

===== Exam =====

During the semester, students can get at most 100 points: 40 points for task solutions and 60 points for exam. To successfully pass the exam, students need to get at least 30 points out of 60, i.e. 50 %.

  * Example of exam for the machine learning part ({{:courses:ae3m33ui:ui-exam-example-ml-cz.pdf|Česky}}, {{:courses:ae3m33ui:ui-exam-example-ml-en.pdf|English}})
  * Example of exam for the intelligent agents part ({{:courses:ae3m33ui:ui-exam-example-ia-cz.pdf|Česky}}, {{:courses:ae3m33ui:ui-exam-example-ia-en.pdf|English}})




===== Literature =====
  - Mařík V., Štěpánková O., Lažanský J. a kol: Umělá inteligence 1-5, Academia, Praha, 1993-2007
  - [AIMA3] Russel S. a Norvig P.: Artificial Intelligence: A Modern Approach (3rd edition), Prentice Hall, 2010
  - [AIMA] Russel S. a Norvig P.: Artificial Intelligence: A Modern Approach (2nd edition), Prentice Hall, 2003
  - [RLAI] Sutton, S. R. Barto, A. G.: Reinforcement Learning: An Introduction, The MIT Press, 1998, [[http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html|available on-line]] 
    * [[https://www.dropbox.com/s/b3psxv2r0ccmf80/book2015oct.pdf?dl=0|Draft of the 2nd edition (Oct 2015)]]
  - Vidal, J. M.: Fundamentals of Multiagent Systems with NetLogo Examples, 2009, [[http://multiagent.com/2009/03/fundamentals-of-multiagent-systems.html|available on-line]]