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Course organization and credit conditions

The lectures takes place in KN:E-301 on Monday 14:30-16:00, see Lecture schedule for details. The labs will take place on Monday, Tuesday and Wednesday, see schedule for details. The first 7 weeks will be focused on lectures, regular labs with homework, the content of remaining 7 weeks will depend on your decision: You can either continue in regular labs and solving homeworks, or you can work on a semestral work of your choice. Lecture in the 13th week is devoted to exam test. The last lecture is poster session, where each team presents its semestral work to others. The points are divided into the three following categories:

  • Common homework from the first 7 weeks HW1, HW2 (20p),
  • Semestral work (40p + 5 bonus points) — OR — compulsory-optional homework HW3, HW4, HW5 (40p),
  • Tests (40p).

Minimum credit requirements:

  • achieve at least one point from each homework (without considering the late submission penalty)
  • achieve at least one point from every test

The final grade will be determined by the total number of points according to the following table

No of points Exam assessment
0-49 F
50- 59 E
60-69 D
70-79 C
80-89 B
90-100 A


All homework will be assigned during the labs, see labs schedule for the assignment dates. The submission of each homework has strict deadline. The number of points achieved from homework will depend on the relative performance of the solution.

Semestral work

Students are encouraged to come up with their own topics of semestral work – just come with something which will be fun for you (and us) and it is at least somehow connected with deep learning. Nevertheless, we have provided Suggestions for the Topics by our teachers for your inspiration. In both cases keep in minds that the amount of teacher's help is restricted by the content of this course. In particular, we can help you with explaining “why your classifier does not generalizes well on a new data”, however we cannot help you with solving problems such as “I have downloaded code from some researcher and I do not know how to compile it” or “I do not know how to build application for mobile phone”. ⇒ Choose the semestral work wisely and make sure that any required knowledge is covered sufficiently by your team members. Semestral work is risky business, in case of failure the number of points can be lower than from compulsory-optional homework, therefore we have decided to provide reward for taking the risk in the form of 5 bonus points. In case of fatal failure, you can always switch to the homework branch.

Suggested size of the team for semestral work is four students. Larger or smaller groups will be allowed in well justified cases (typically it is required to explain role of each student cooperating in the team). You are strongly encouraged to start working on the semestral work from the very beginning (first week), since most of the semestral work topics contains preparation works, which does not require any knowledge gained during the course. To start working, just create a team and present the topic and work-plan to the head of the labs (vacekpa2@fel.cvut.cz). In case of both-side-agreement, you can start working immediately.

The presentation of semestral works takes place in the 14 week. The work will be presented to all students on the poster session during the Monday lecture. In addition to this, there will be 10 minute long oral presentations, which will take place during the labs. Evaluation is based on lecturer's and student's voting.


There will be two assessments in total: a mid-term and an exam. These are worth 20 points each, giving a maximum of 40 achievable points in total. Both will take place during Monday Lectures, see schedule for planned weeks. Competencies required for passing the test will be summarized in the end of each lecture.


http://cmp.felk.cvut.cz/~zimmerk Karel Zimmermann is the main lecturer of ViR. He is currently associate professor at the Czech Technical University in Prague. He received his PhD degree in cybernetics in 2008. He worked as postdoctoral researcher with the Katholieke Universiteit Leuven (2008-2009) in the group of prof Luc van Gool. His current H-index is 13 (google-scholar) and he serves as a reviewer for major journals such as TPAMI or IJCV and conferences such as CVPR, ICCV, IROS. He received the best lecturer award in 2018, the best reviewer award at CVPR 2011 and the best PhD work award in 2008. His journal paper has been selected among 14 best research works representing Czech Technical University in the government evaluation process (RIV). Since 2010 he has been chair of Antonin Svoboda Award (http://svobodovacena.cz). He was also with the Technological Education Institute of Crete (2001), with the Technical University of Delft (2002), with the University of Surrey (2006). His current research interests include learnable methods for robotics.

http://cmp.felk.cvut.cz/~vacekpa2 Patrik Vacek is the head of the VIR lab. He is the third year PhD student at the Department of Cybernetics. His main research interests include learning for self-driving cars from unlabelled data.

http://cmp.felk.cvut.cz/~azayetey Teymur Azayev is second lab tutor. He is the third year PhD student at the Department of Cybernetics. His current interests include dynamic robot locomotion using deep learning.


We want students to work individually, therefore any plagiarism in codes, homework or reports will be punished. We strongly urge each student to read what is/is not plagiarism - we believe that many students will be surprised. In any case, it is not permitted to use the work of your colleagues or predecessors. Each student is responsible for ensuring that his work does not get into the hands of other colleagues. In the case of multiple submission of the same work, all involved students will be penalized, including those who gave the work available to others.

The above does not apply to semestral works (2nd part of the semester) in which you will be working as a team and can cooperate between teams as well.

courses/b3b33vir/start.txt · Last modified: 2021/11/10 11:57 by zimmerk