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B(E)3M33UI - Artificial intelligence

The course deepens and enriches knowledge of AI gained in the bachelor course Cybernetics and Artificial Intelligence. Students will get an overview of other methods used in AI, and will get a hands-on experience with some of them. They will master other required abilities to build intelligent agents. By applying new models, they will reiterate the basic principles of machine learning, techniques to evaluate models, and methods for overfitting prevention. They will learn about planning and scheduling tasks, and about methods used to solve them. The student will also get acquainted with the basics of probabilistic graphical models, Bayesian networks, and Markov models, and will learn their applications. Part of the course will introduce students to the area of again popular neural networks, with an emphasis on new methods for deep learning.

Lectures | Literature | Labs | Projects | Students exam questions | BRUTE | Forum | Prerequisities

  • 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.
  • For an average student, this corresponds to 2 hours of lecture, 2 hours of labs and 5-6 hours of work at home each week!

In accordance with the current anti-coronavirus measures, course B(E)3M33UI is taught in a non-standard, distant way. Current plans (March 19) are as follows:

  • Lectures:
    • Delivered via BigBlueButton Conference Rooms functionality in BRUTE in standard lecture times (Tuesdays 12:45-14:15). Recordings will be available in BRUTE. (See the tutorial video for students.)
    • Supported by self-study based on the lecture slides, suggested reading material, and suggested lecture videos from online courses where possible.
  • Seminars/labs:
    • The tasks can be fulfilled at home based on the prepared materials, submission via BRUTE.
    • Teachers will be present in the standard seminar times (Tuesdays 14:30-16:00 and 16:15-17:45) in BigBlueButton conference rooms for questions and consultations.
  • Consultations:
    • Questions can be posted on discussion forum.
    • Teachers are available on email, one-to-one consultations possible via Skype, WhatsUp, etc., as negotiated between the teacher and the student.
  • Deadlines were postponed by 14 days.
  • The attendance of labs is of course not required.

Assessment requirements

  • Lectures are not obligatory, but their attendance is highly recommended.
  • Computer labs attendance is required. At most 2 absences in computer labs will be tolerated.
  • During the semester, students can gain up to 50 points:
    • 3 points for accepted exam questions (all 3 points required for assessment) submitted exam questions will be rewarded by 1 bonus point each
    • 7 10 points for lab tasks completed and submitted in time (none required for assessment)
    • 20 points for semestral task 1 (at least 10 points required for assessment).
    • 20 points for semestral task 2 (at least 10 points required for assessment).
  • Students must get at least 25 points to get the assessment and be allowed to enter the exam.

Late policy

  • No points will be given for late submission to lab tasks.
  • Late solutions to semestral tasks will be penalized by 4 points for each started week of delay (details in BRUTE).


During the semester, including the exam, students can get at most 100 points: 50 points for the semestral projects and 50 points for the exam. To successfully pass the exam, students need to get at least 25 points out of 50, i.e. 50 %.

  • Half of the exam will be made of questions proposed by the students themselves during the semester.
  • The list of accepted student questions (will be available after some questions will be accepted).
  • We also provide list of competencies which can serve as an inspiration for possible exam questions.


We use the usual grading table:

Points >=90 <80, 90) <70, 80) <60, 70) <50, 60) (0, 50)
Grade A B C D E F


Lecturers: Petr Pošík, Radek Mařík

Lab instructor: Petr Pošík, Jiří Spilka

Consultations by appointment (after previous agreement by email).

courses/ui/start.txt · Last modified: 2020/05/27 11:41 by xposik