B(E)3M33UI - Artificial intelligence

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.

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!

The teaching in course B(E)3M33UI starts in a distance way, online.

BigBlueButton in BRUTE:
- Lectures and tutorials/seminars will be delivered via BigBlueButton Conference Rooms functionality in BRUTE in standard lecture/seminar times. (See the tutorial video for students.)
- All students will obtain an invitation with the link to BBB room before the lecture/seminar. The link is also available in BRUTE.
- Recordings are available in BRUTE.
Lectures:
- Not obligatory, but their attendance is highly recommended.
Seminars/labs:
- The tasks can be fulfilled at home based on the prepared materials. Solution 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 explnations, questions and consultations.
- The attendance of labs is not required, but it is a very suitable time to work on the tasks with the option to ask the teacher.
Consultations:
- Questions shall be asked primarilly at the lab sessions.
- Questions can be posted on discussion forum.
- Teachers are available on email, one-to-one consultations possible via MS Teams, BBB, etc., as negotiated between the teacher and the student.

Assessment requirements

During the semester, students can gain up to 50 points:
- 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 take part in 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).

Exam

Exam is worth another 50 points.
To successfully pass the exam, students need to get at least 25 points out of 50, i.e. 50 %.
We provide list of competencies which can serve as an inspiration for possible exam questions.

Grading

During the semester, students can get at most 100 points: 50 points for the work during semester and 50 points for the exam. 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

Contacts

Lecturers: Petr Pošík, Radek Mařík

Lab instructor: Petr Pošík, Jiří Spilka

Consultations by appointment (after previous agreement by email).

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