/*{{:courses:b3b33urob:uceni_robotu_v3.png?1000|}} */ {{:courses:b3b33urob:robots_learning.png?800|}} ~~NOTOC~~ /* {{:courses:b3b33urob:uceni_robotu_v2.png?1000|}} {{ :courses:b3b33vir:vir_title.png?1000 |}}*/ /*{{ :courses:b3b33vir:vir_title.png?1000 |}} {{ :courses:b3b33vir:vir_title.png?1000 |}} {{ :courses:b3b33vir:robots.png?800 |}} */ /*====== B3B33VIR – Vidění robotu ======*/ /* {{ youtube>h6Srq8kpREA?medium}} */ ====== Links ====== * Anketa results around 1.20: [[https://anketa.is.cvut.cz/html/anketa/results/semesters/B231/surveys/11/courses/B3B33UROB|2023]], [[https://anketa.is.cvut.cz/html/anketa/results/semesters/B221/surveys/11/courses/B3B33VIR| 2022]], [[https://anketa.is.cvut.cz/html/anketa/results/semesters/B211/surveys/11/courses/B3B33VIR|2021]], [[https://anketa.is.cvut.cz/html/anketa/results/semesters/B201/surveys/11/courses/B3B33VIR|2020]], [[https://www.fel.cvut.cz/cz/anketa/archiv/anketa.B181/teachers/zimmerk/course/B3B33VIR/index.html| 2019]], [[http://www.fel.cvut.cz/cz/aktuality/2019/anketa-zima-odmena.html | Dean's award ]] * Resources: [[courses:b3b33urob:resources:start| Servers]], [[https://pytorch.org/tutorials/beginner/ptcheat.html|pytorch cheatsheet]], [[http://www.deeplearningbook.org| Deep Learning book (Goodfellow et al.)]] * [[https://github.com/urob-ctu| Course github]] * [[https://urob-ctu.github.io/docs| Course pages]] * [[https://intranet.fel.cvut.cz/cz/education/rozvrhy-ng.B251/public/html/predmety/66/51/p6651806.html | Schedule]] * [[https://cw.felk.cvut.cz/forum/forum-1883.htmll|Diskuzni forum]] * [[https://www.youtube.com/watch?v=h5BmWo5_sc8&list=PLSQl0a2vh4HC5feHa6Rc5c0wbRTx56nF7&index=59 | Explained multivariable calculus (gradient, hessian, taylor approximation, ...)]] * Student's works [[https://www.youtube.com/watch?v=NUYFi4N1D70&list=PLhGZ28DZufNowLsCiNs_i5V2nw0OmGY7w|UROB 2023]] /* Student's works [[https://www.youtube.com/watch?v=NUYFi4N1D70&list=PLhGZ28DZufNowLsCiNs_i5V2nw0OmGY7w|UROB 2023]] */ /*======== Spooky lecturer by David Korcak ======== {{:courses:b3b33urob:kz_halloween_lecturer.png?400|}}{{:courses:b3b33urob:kz_halloween_lecturer.png?200|}}{{:courses:b3b33urob:kz_halloween_lecturer.png?100|}}{{:courses:b3b33urob:kz_halloween_lecturer.png?50|}} */ /*======== Halloween creative works of UROB students ======== Selected winners:\\ {{:courses:b3b33urob:img_0478.jpg?200|}} {{:courses:b3b33urob:img_0477.jpg?300|}} {{:courses:b3b33urob:img_0479.jpg?300|}} All works: \\ {{:courses:b3b33urob:img_0482.jpg?600|}} */ /* ======== Distant teaching ======== In case of distant teaching, see regularly updated [[https://www.fel.cvut.cz/en/covid/ | list of coronavirus news]] for more details. * **[[courses:b3b33vir:lectures:start|Lectures]]** are taught online using BBB client which runs directly in the web browser. All students will receive an email invitation with the link to the conference room. Lectures are recorded and made available [[courses:b3b33vir:lectures:start|here]]. * **[[courses:b3b33vir:tutorials:start|Labs]]** are taught online or replaced by offline tutorials. Students will be informed about the form of each particular lab not later than the beginning of the corresponding week. */ ====== Course organization and credit conditions ====== The [[courses:b3b33urob:lectures:start|lectures]] take place in KN:E-301 on Monday 14:30-16:00 and the [[courses:b3b33urob:tutorials:|labs]] take place in KN:E-230 on Tuesday and Wednesday. /*The first seven weeks will be focused on lectures and regular labs with homework; the content of the remaining seven weeks will depend on your decision: You can either continue in regular labs and solve homework, or you can work on your own semestral work.*/ Tests take place during lectures. The points are divided as follows: * Homeworks: 5x10 points (50 points in total) * Tests: midterm test (25p), exam test (25p) **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 | ====== Homework ====== All homework will be assigned during the labs; see the lab schedule for the assignment dates. The submission of each homework has a strict deadline. The number of points achieved from the homework will depend on the relative performance of the solution. ====== Semestral work ====== In well-justified cases, students can replace some homeworks with semestral work supervised by an external advisor (any faculty member). If you're interested, please speak with me after the lecture. The project must be related to deep learning and aligned with the content of this course. We can assist with questions such as “Why is my classifier failing to generalize to new data?” but cannot provide support for issues like “I downloaded someone else's code and can’t compile it” or “How do I build a mobile app?”. Semester project presentations will take place in the 14th week during lab sessions. Presentations will be 10 minutes long, and evaluations will be conducted by both lecturers and students. ====== Tests ====== 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. The competencies required for passing the test will be summarized at the end of each lecture. ====== Lecturers ====== {{:courses:b3b33vir:karel_zimmermann.png?160 |http://cmp.felk.cvut.cz/~zimmerk}} [[http://cmp.felk.cvut.cz/~zimmerk/|Karel Zimmermann]] is the main lecturer of ViR. He is currently an 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 16 (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 the 14 best research works representing Czech Technical University in the government evaluation process (RIV). Since 2010 he has been chair of the Antonin Svoboda Award ([[http://svobodovacena.cz]]). He was also with the Technological Education Institute of Crete (2001), the Technical University of Delft (2002), and the University of Surrey (2006). His current research interests include learnable methods for robotics. \\ \\ \\ {{:courses:b3b33urob:kucera_ales.jpg?160 |}} Aleš Kučera is the lab tutor. He is a PhD student in Cybernetics and Robotics. His main focus is on differentiable physics simulations. () \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ {{:courses:b3b33urob:vlk_jan.jpg?160 |}} [[https://vlk-jan.github.io/|Jan Vlk]] is the lab tutor. He is a PhD student in Cybernetics and Robotics. His main focus is on autonomous control based on observed terrain properties. () \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ {{:courses:b3b33urob:capekda4.jpg?160 |}} David Čapek is the lab tutor. He is a master's student in Cybernetics and Robotics. () \\ \\ \\ \\ \\ \\ \\ \\ \\ /*====== Lecturers ====== | {{:courses:b3b33vir:karel_zimmermann.png?170 |http://cmp.felk.cvut.cz/~zimmerk}} | {{:courses:b3b33vir:TA.png?225 ||http://cmp.felk.cvut.cz/~azayetey}} | {{:courses:b3b33vir:patrik_vacek.png?160 |http://cmp.felk.cvut.cz/~vacekpa2}} | {{:courses:b3b33vir:ota.jpg?200 |http://cmp.felk.cvut.cz/~jasekota}} | |[[http://cmp.felk.cvut.cz/~zimmerk/|Karel Zimmermann]] | [[http://cmp.felk.cvut.cz/~azayetey/|Teymur Azayev]] | [[http://cmp.felk.cvut.cz/~vacekpa2/|Patrik Vacek]] | [[http://cmp.felk.cvut.cz/~jasekota/|Otakar Jašek]] |*/ ====== Plagiarism ====== 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 [[:help:common:plagiaty_opisovani|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 submissions 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:b3b33urob:npo-publicity-logo.png?1000|}}