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.