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: A3M33UI, AE3M33UI
  • Schedule A3M33UI, AE3M33UI

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 (Česky, English)
  • Example of exam for the intelligent agents part (Česky, English)

Literature

  1. Mařík V., Štěpánková O., Lažanský J. a kol: Umělá inteligence 1-5, Academia, Praha, 1993-2007
  2. [AIMA3] Russel S. a Norvig P.: Artificial Intelligence: A Modern Approach (3rd edition), Prentice Hall, 2010
  3. [AIMA] Russel S. a Norvig P.: Artificial Intelligence: A Modern Approach (2nd edition), Prentice Hall, 2003
  4. [RLAI] Sutton, S. R. Barto, A. G.: Reinforcement Learning: An Introduction, The MIT Press, 1998, available on-line
  5. Vidal, J. M.: Fundamentals of Multiagent Systems with NetLogo Examples, 2009, available on-line
 
Groups:
courses/ae3m33ui/start.txt · Last modified: 2016/05/20 12:02 by xposik