Faculty elective subjects
Winter semester 2025/2026
The second-cycle study plan includes modules in which the student has the possibility of choosing subjects to the extent specified in the study program. The requirement defined as „elective subjects” complements the curriculum requirements.
Enrollment in elective subjects is conducted electronically via the MojaPG system, according to the instructions:
The student’s responsibility is to:
- enroll in the elective subjects (all of the components) for a minimum of 3 ECTS points (in both the winter and summer semesters), and in the case of Biomedical Engineering, for 6 ECTS points (selection only in the winter semester). The condition for passing an elective subject will be enrolling in and passing all of its components (class groups – laboratories, lecture etc.);
- choose the appropriate class groups on the schedule (this happens during the enrollment proces, but it is neccessary to pay attention to it, especially if the given classes would conflict with the mandatory classes of student’s specialisation);
- optional change of class groups for mandatory subjects (you can transfer to other groups in the same type of classes if alternetive groups are possible in the schedule and classes on another day suit you better).
Registration for the elective subjects takes place in the week preceding the start of the semester.
The following elective subjects are planned for the winter semester of the academic year 2025/2026:
Subject from the field of study |
Name |
ECTS |
Zal/egz |
Hours |
W |
C |
L |
P |
S |
Person responsible for subject |
Max. numer of people |
ACiR |
Team Strategies |
2 |
EGZ |
30 h |
15 |
15 |
dr inż. Tomasz Białaszewski |
18 |
|||
EiT (KSSR) |
Physical Layer Security |
1 |
ZAL |
15 h |
15 |
dr inż. Jarosław Magiera |
50 |
||||
EiT (KSSR) |
Artificial Intelligence in Wireless Communication |
2 |
ZAL |
30 h |
15 |
15 |
dr Krzysztof Cwalina |
18 |
|||
EiT (KSSR) |
Next Generation Radio Communication Systems |
2 |
EGZ |
30 h |
30 |
dr inż. Sławomir Gajewski |
50 |
||||
INF (KASK) |
Intelligent Information Services |
3 |
EGZ |
45 h |
15 |
30 |
dr hab. inż. Julian Szymański |
18 |
|||
INF (KASK) |
Deep Learning in Computer Vision |
2 |
EGZ |
30 h |
15 |
15 |
dr inż. Adam Brzeski |
18 |
|||
INF (cooperation with IBM) |
AI Technology Deep Dive (PL + ANG; Prerequisites: programming skills, Python - basic skills) |
3 |
ZAL |
45 h |
30 |
15 |
dr hab. inż. Agnieszka Landowska |
36 |
Detailed information about individual subjects can be found in the subject cards, in the ECTS Information Catalogue, or directly from the persons responsible for the subjects.