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Viktoria Mirsalova
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8
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I., I.II., II.
on-site learning
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I., II.
1317979
1
C
instructor
Viktoria
Mirsalova
Viktoria Mirsalova
mirsalova1@uniba.sk
Russian, Slovak
RJ
Russian
SK
Slovak
Russian, Slovak
Z
1
7
<_C_>
Goal
To master the fundamentals of general Russian. The language level is A1.
Learning the Cyrillic (Russian) alphabet, gaining basic language competence, building up skills and confidence in dealing with unfamiliar authentic and semi-authentic texts.
<_L_>
Recommended literature
The textbook: : Точка Ру A1 (Ольга Долматова, Екатерина Новачац), рracovné karty Падежи 1 (Л.С. Безкоровайная, В.Е. Штыленко).
<_P_>
Continuous assessment
test
<_SO_>
Brief outline of the course
To master the fundamentals of general Russian. The language level is A1.
Learning the Cyrillic (Russian) alphabet, gaining basic language competence, building up skills and confidence in dealing with unfamiliar authentic and semi-authentic texts.
The subject provides a course in Russian language for beginners.
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
Basic communication in Russian, developing other Russian language skills - listening comprehension, reading and writing.
A
431
57.77
B
124
16.62
C
83
11.13
D
31
4.16
E
13
1.74
FX
64
8.58
746
6
28193
B
1-MXX-162
FMFI.KJP/1-MXX-162/00
Russian Language (2)
2
Evaluation
practicals
2
26
2, 8
(Single degree study, master II. deg., full time form)
L
1, 4
Viktoria Mirsalova
16.02.2026
20.06.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
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I., I.II., II.
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1317979
1
C
instructor
Viktoria
Mirsalova
Viktoria Mirsalova
mirsalova1@uniba.sk
Russian, Slovak
RJ
Russian
SK
Slovak
Russian, Slovak
L
2
8
<_C_>
Goal
To master the fundamentals of general Russian.
Learning the Cyrillic (Russian) alphabet, gaining basic language competence, building up skills and confidence in dealing with unfamiliar authentic and semi-authentic texts.
<_L_>
Recommended literature
Textbook: Точка Ру A1 (Ольга Долматова, Екатерина Новачац), рracovné karty Падежи 1 (Л.С. Безкоровайная, В.Е. Штыленко).
<_P_>
Continuous assessment
test
<_SO_>
Brief outline of the course
To master the fundamentals of general Russian.
Learning the Cyrillic (Russian) alphabet, gaining basic language competence, building up skills and confidence in dealing with unfamiliar authentic and semi-authentic texts.
The subject continues the program of Russian language (1) and provides a course of Russian for beginners.
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
Basic communication in Russian, developing other Russian language skills - listening comprehension, reading and writing.
A
278
63.91
B
70
16.09
C
39
8.97
D
17
3.91
E
4
0.92
FX
27
6.21
435
6
95117
B
1-MXX-233
FMFI.KJP/1-MXX-233/13
English Conversation Course (1)
2
Evaluation
practicals
2
26
1, 3, 7, 9
(Single degree study, master II. deg., full time form)
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1, 2, 4, 5
Mgr. Aneta Barnes
16.02.2026
11.04.2024
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
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I., I.II., II.
on-site learning
true
I., II.
1304488
1
C
instructor
Aneta
Barnes
Mgr. Aneta Barnes
barnes1@uniba.sk
English, Slovak
EN
English
SK
Slovak
English, Slovak
Z
1
3
7
9
<_C_>
Goal
Continual improvement of all language skills focused on communication/speaking, listening comprehension and writing. The emphasis is on discourse, lexicology and morphology, word-bank broadening of communicational English as well as English for specific purposes appropriate for university students. This course is a follow up of the previously taught ESP course.
<_L_>
Recommended literature
Appropriate study material is supplied based on the participants’level of English by the lecturer.(Sources- The Guardian, The Herald Morning Sun. The Nine News, The West Australian, BBC News and podcasts, CNN podcasts).
<_P_>
Continuous assessment
tests, oral presentations, essays
<_PA_>
Conditions for completion of course
tests, presentations, essays
Course prerequisites:
https://fmph.uniba.sk/microsites/kjp/katedra-jazykovej-pripravy/poziadavky-na-udelenie-priebezneho-hodnotenia-aj1aj2aj3-ostatne-kurzy/
<_SO_>
Brief outline of the course
This course’s focus is to broaden spoken/communicational English for students with B2/C1 level of English knowledge.
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
Continual improvement of all language skills focused on communication/speaking, listening comprehension and writing. The emphasis is on discourse, lexicology and morphology, word-bank broadening of communicational English as well as English for specific purposes appropriate for university students. This course is a follow up of the previously taught ESP course.
<_Z_>
Final assessment
tests
A
246
77.36
B
28
8.81
C
14
4.4
D
4
1.26
E
3
0.94
FX
23
7.23
318
6
95118
B
1-MXX-234
FMFI.KJP/1-MXX-234/13
English Conversation Course (2)
2
Evaluation
practicals
2
26
2, 4, 8, 10
(Single degree study, master II. deg., full time form)
L
1, 2, 4, 5
Mgr. Aneta Barnes
16.02.2026
11.04.2024
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
practicals
2
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prezenčná
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3
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3
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2
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2
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2
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4
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5
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4
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5
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4
on-site learning
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5574
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1304488
1
C
instructor
Aneta
Barnes
Mgr. Aneta Barnes
barnes1@uniba.sk
English, Slovak
EN
English
SK
Slovak
English, Slovak
L
2
4
8
10
<_C_>
Goal
Continual improvement of all language skills focused on communication/speaking, listening comprehension and writing. The emphasis is on discourse, lexicology and morphology, word-bank broadening of communicational/spoken English as well as English for specific purpose appropriate for university students. This course is a follow up of the Conversational English course 1.
<_L_>
Recommended literature
Appropriate study material is supplied based on the participants’level of English by the lecturer.(Sources- The Guardian, The Herald Morning Sun. The Nine News, The West Australian, BBC News and podcasts, CNN podcasts).
<_P_>
Continuous assessment
tests, oral presentations, essays
<_PA_>
Conditions for completion of course
tests, oral presentations, essays
Course prerequisites:
https://fmph.uniba.sk/microsites/kjp/katedra-jazykovej-pripravy/poziadavky-na-udelenie-priebezneho-hodnotenia-aj1aj2aj3-ostatne-kurzy/
<_SO_>
Brief outline of the course
This course’s focus is to broaden spoken/communicational English for students with B2/C1 level of English knowledge( Upper-Intermediate/Lower Advanced).
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
Continual improvement of all language skills focused on communication/speaking, listening comprehension and writing. The emphasis is on discourse, lexicology and morphology, word-bank broadening of communicational/spoken English as well as English for specific purpose appropriate for university students. This course is a follow up of the Conversational English course 1.
<_Z_>
Final assessment
tests
A
165
82.09
B
18
8.96
C
5
2.49
D
2
1.0
E
0
0.0
FX
11
5.47
201
6
29712
B
1-MXX-261
FMFI.KJP/1-MXX-261/00
Russian Language (3)
2
Evaluation
practicals
2
26
3, 9
(Single degree study, master II. deg., full time form)
Z
2, 5
Viktoria Mirsalova
16.02.2026
20.06.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
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2
26
prezenčná
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2
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5
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4037
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2
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2
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2
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5
on-site learning
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5
on-site learning
9
967
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Z
5
on-site learning
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970
mFFP
Plasma Physics
Z
5
on-site learning
9
971
mFTL
Solid State Physics
Z
2
on-site learning
3
976
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2
on-site learning
3
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2
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5
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9
3965
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Z
2
on-site learning
3
3971
mFPE
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2
on-site learning
3
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mFJFa
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5
on-site learning
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4384
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5
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9
4386
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5
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5
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I., I.II., II.
on-site learning
true
I., II.
1317979
1
C
instructor
Viktoria
Mirsalova
Viktoria Mirsalova
mirsalova1@uniba.sk
Russian, Slovak
RJ
Russian
SK
Slovak
Russian, Slovak
Z
3
9
<_C_>
Goal
Learning the handwritten Russian (Russian Cursive Cyrillic), developing further language skills, gaining knowledge of Russian culture, history and way of life, pre-intermediate to intermediate grammar and vocabulary.
<_L_>
Recommended literature
Точка Ру A2 (Ольга Долматова, Екатерина Новачац) a Short Stories in Russian (Olly Richards, Alex Rowlings)
<_P_>
Continuous assessment
test
<_SO_>
Brief outline of the course
Learning the handwritten Russian (Russian Cursive Cyrillic), developing further language skills, gaining knowledge of Russian culture, history and way of life, pre-intermediate to intermediate grammar and vocabulary.
The course "Russian for Intermediate Students" is a follow-up to "Russian for Beginners". The subject of the course is general Russian in the range appropriate to the given level.
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
Basic communication in Russian, developing other Russian language skills - listening comprehension, reading and writing.
A
148
68.84
B
38
17.67
C
20
9.3
D
5
2.33
E
0
0.0
FX
4
1.86
215
6
29714
B
1-MXX-262
FMFI.KJP/1-MXX-262/00
Russian Language (4)
2
Evaluation
practicals
2
26
4, 10
(Single degree study, master II. deg., full time form)
L
2, 5
Viktoria Mirsalova
16.02.2026
20.06.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
practicals
2
26
prezenčná
C
4776
TEF
Technical Physics
L
2
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4
4898
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Renewable Energy Sources and Environmental Physics (Conversion Programme)
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2
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954
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2
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956
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Managerial Mathematics
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2
on-site learning
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967
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5
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2
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5
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2
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4727
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2
on-site learning
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971
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5
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1017
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2
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5
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5
on-site learning
10
3006
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5
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Plasma Physics
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5
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4378
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2
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2
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4384
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Optics, Lasers and Optical Spectroscopy
L
2
on-site learning
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Economic and Financial Mathematics
L
2
on-site learning
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3964
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Bioinformatics
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2
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4894
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Bioinformatics (Conversion Programme)
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2
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5008
upINGE
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L
2
on-site learning
4
5525
upFYBI
učiteľstvo fyziky a biológie
L
2
on-site learning
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5539
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2
on-site learning
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5620
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Biomedical Physics
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2
on-site learning
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954
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Probability and Mathematical Statistics
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2
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3509
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5
on-site learning
10
4037
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Computer Graphics and Geometry (Conversion Programme)
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2
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4376
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2
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4385
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Computer Graphics and Geometry
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5
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Probability and Mathematical Statistics
L
5
on-site learning
10
5574
muINGE
Teaching Computer Science and Geography
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5
on-site learning
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933
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Teaching Mathematics and Computer Science
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2
on-site learning
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935
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Teaching Physics and Computer Science
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2
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936
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Teaching Mathematics and Descriptive Geometry
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2
on-site learning
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958
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Insurance Mathematics
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2
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966
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Managerial Mathematics
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2
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974
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Physics
L
2
on-site learning
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4889
DAV
Data Science
L
2
on-site learning
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4891
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Data Science (Conversion Programme)
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2
on-site learning
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4896
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Physics (Conversion Programme)
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2
on-site learning
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Theoretical Physics
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5
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956
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2
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5
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Applied Computer Science (Conversion Programme)
L
5
on-site learning
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3007
muINBI
Teaching Computer Science and Biology
L
2
on-site learning
4
3965
mEMM
Mathematical Economics, Finance and Modelling
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2
on-site learning
4
4375
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Environmental Physics, Renewable Energy Sources and Meteorology and Climatology
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2
on-site learning
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Solid State Physics
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2
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4382
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Managerial Mathematics
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2
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Managerial Mathematics
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5
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5620
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Biomedical Physics
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5
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5687
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Data Science
L
2
on-site learning
4
932
upMAFY
Teaching Mathematics and Physics
L
2
on-site learning
4
969
INF
Computer Science
L
2
on-site learning
4
975
BMF
Biomedical Physics
L
2
on-site learning
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968
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Computer Science
L
2
on-site learning
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970
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Plasma Physics
L
5
on-site learning
10
1019
mAIN
Applied Computer Science
L
5
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2979
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Mathematics
L
2
on-site learning
4
2980
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Optics, Lasers and Optical Spectroscopy
L
5
on-site learning
10
3007
muINBI
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L
5
on-site learning
10
3508
muFYIN/k
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L
2
on-site learning
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3508
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L
5
on-site learning
10
3971
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L
2
on-site learning
4
4037
mMPG/k
Computer Graphics and Geometry (Conversion Programme)
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5
on-site learning
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5
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4381
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5
on-site learning
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4386
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2
on-site learning
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2
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2
on-site learning
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5123
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2
on-site learning
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5574
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2
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5
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5
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2
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3509
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L
2
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4
3966
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L
5
on-site learning
10
4371
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Applied Computer Science
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5
on-site learning
10
4373
mFBMa
Biomedical Physics
L
2
on-site learning
4
4383
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Mathematics
L
2
on-site learning
4
4383
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Mathematics
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5
on-site learning
10
5123
muINAN
Teaching Computer Science and English Language and Literature
L
5
on-site learning
10
931
upMATV
Teaching Mathematics and Physical Education
L
2
on-site learning
4
934
upINBI
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L
2
on-site learning
4
980
AIN
Applied Computer Science
L
2
on-site learning
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4777
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Technical Physics
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2
on-site learning
4
4900
TEF/k
Technical Physics (Conversion Programme)
L
2
on-site learning
4
965
mINF/k
Computer Science (Conversion Programme)
L
5
on-site learning
10
968
mINF
Computer Science
L
5
on-site learning
10
970
mFFP
Plasma Physics
L
2
on-site learning
4
976
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Biomedical Physics
L
5
on-site learning
10
978
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Astronomy and Astrophysics
L
2
on-site learning
4
1018
muMAIN
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L
5
on-site learning
10
2596
mAIN/k
Applied Computer Science (Conversion Programme)
L
2
on-site learning
4
3006
muFYIN
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L
5
on-site learning
10
3507
muMADG/k
Teaching Mathematics and Descriptive Geometry (Conversion Programme)
L
5
on-site learning
10
3971
mFPE
Physics of the Earth
L
5
on-site learning
10
4374
mEMMa
Mathematical Economics, Finance and Modelling
L
2
on-site learning
4
4375
mEOMa
Environmental Physics, Renewable Energy Sources and Meteorology and Climatology
L
5
on-site learning
10
4378
mINFa
Computer Science
L
5
on-site learning
10
4384
mFOSa
Optics, Lasers and Optical Spectroscopy
L
5
on-site learning
10
4385
mMPGa
Computer Graphics and Geometry
L
2
on-site learning
4
4386
mMPGa/k
Computer Graphics and Geometry (Conversion Programme)
L
5
on-site learning
10
5688
mBIN
Bioinformatics
L
2
on-site learning
4
I., I.II., II.
on-site learning
true
I., II.
1317979
1
C
instructor
Viktoria
Mirsalova
Viktoria Mirsalova
mirsalova1@uniba.sk
Russian, Slovak
RJ
Russian
SK
Slovak
Russian, Slovak
L
4
10
<_C_>
Goal
Learning the handwritten Russian (Russian Cursive Cyrillic), developing further language skills, gaining knowledge of Russian culture, history and way of life, pre-intermediate to intermediate grammar and vocabulary.
<_L_>
Recommended literature
Точка Ру A2 (Ольга Долматова, Екатерина Новачац) a Short Stories in Russian (Olly Richards, Alex Rowlings)
<_P_>
Continuous assessment
test
<_SO_>
Brief outline of the course
Learning the handwritten Russian (Russian Cursive Cyrillic), developing further language skills, gaining knowledge of Russian culture, history and way of life, pre-intermediate to intermediate grammar and vocabulary.
The course "Russian for Intermediate Students" is a follow-up to "Russian for Beginners". The subject of the course is general Russian in the range appropriate to the given level.
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
Learning the handwritten Russian (Russian Cursive Cyrillic), developing further language skills, gaining knowledge of Russian culture, history and way of life, pre-intermediate to intermediate grammar and vocabulary.
A
114
74.51
B
22
14.38
C
11
7.19
D
4
2.61
E
1
0.65
FX
1
0.65
153
6
92161
B
2-EFM-117
FMFI.KAMŠ/2-EFM-117/12
Convex Optimisation
3
Evaluation
lecture
2
26
2, 4
(Single degree study, master II. deg., full time form)
L
1, 2
doc. RNDr. Mária Trnovská, PhD., Mgr. Jakub Hrdina, PhD.
16.02.2026
14.07.2025
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
C
4378
mINFa
Computer Science
L
1
on-site learning
2
954
mPMS
Probability and Mathematical Statistics
L
2
on-site learning
4
4383
mMATa
Mathematics
L
1
on-site learning
2
4374
mEMMa
Mathematical Economics, Finance and Modelling
L
1
on-site learning
2
965
mINF/k
Computer Science (Conversion Programme)
L
1
on-site learning
2
954
mPMS
Probability and Mathematical Statistics
L
1
on-site learning
2
4387
mPMSa
Probability and Mathematical Statistics
L
2
on-site learning
4
968
mINF
Computer Science
L
1
on-site learning
2
2979
mMAT
Mathematics
L
1
on-site learning
2
4387
mPMSa
Probability and Mathematical Statistics
L
1
on-site learning
2
3965
mEMM
Mathematical Economics, Finance and Modelling
L
1
on-site learning
2
II.
on-site learning
true
II.
1051883
1
P
lecturer
Mária
Trnovská
doc. RNDr. Mária Trnovská, PhD.
trnovska2@uniba.sk
English, Slovak
1209058
2
P
lecturer
Jakub
Hrdina
Mgr. Jakub Hrdina, PhD.
hrdina8@uniba.sk
English, Slovak
EN
English
SK
Slovak
English, Slovak
L
2
4
<_C_>
Goal
Introduce the theory, methods and applications of convex optimization.
<_L_>
Recommended literature
M. Trnovská: Konvexná optimalizácia, elektronický text.
Boyd, Vandenberghe: Convex Optimization, Cambridge Univ.Press 2004
CVX: Matlab Software for Disciplined Convex Programming www.stanford.edu/~boyd/cvxbook
Ben-Tal, Nemirovski: Lectures on Modern Convex Optimization, SIAM 2001
<_O_>
Content prerequisite
Nonlinear programming, Linear programming
<_P_>
Continuous assessment
team homework, test
<_PA_>
Conditions for completion of course
Ongoing assessment: homework assignments (20%), in-class tests (30%)
Final assessment: semester project (20%), final exam (30%)
Grading A 91%, B 81%, C 71%, D 61%, E 51%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_S_>
Course syllabus
Convex optimization problems in standard form
(Convex functions, problems, duality, KKT optimality conditions)
Generalization of standard convex problems
(Convex cones, conic programs, transformation of standard convex problems into a conic form)
Conic convex problems
(Basic conic linear programming classes, LP, SDP, SOCP,.. and relations between them, conic representable functions and problems)
Geometry of convex cones
(Proper cones, dual and self dual cones, properties)
Duality theory for conic linear programs
(Dual problem derivation, Generalized Farkas Lemma, Duality theory for conic linear problems, optimality conditions)
Applications of convex conic problems
(Portfolio optimization, Optimal experiment design, Minimal surface problem, Linear and quadratic discrimination)
Conic relaxations
(Non-convex quadratic problems relaxation, MAX-CUT)
Interior point methods
<_SO_>
Brief outline of the course
Convex optimization problems in standard form
Generalization of standard convex problems
Conic convex problems (SDP, SOCP,..)
Geometry of convex cones
Duality theory for conic linear programs
Applications of convex conic problems
Conic relaxations
Interior point methods
<_VH_>
Weighting of course assessment (continuous/final)
50% /50%
<_VV_>
Learning outcomes
Student learn the basic theory of convex analysis and convex (conic) optimization,
basic classes of convex conic programming, and methods for solving them, they learn the duality theory of conic linear programming. They are able to use Matlab and CVX (cvxopt) modeling system for solving convex problems, they are able to solve various practical problems and applications.
<_Z_>
Final assessment
Project
A
96
68.57
B
21
15.0
C
13
9.29
D
3
2.14
E
4
2.86
FX
3
2.14
140
6
133820
B
2-EFM-140
FMFI.KAMŠ/2-EFM-140/22
SQL Databases
5
Evaluation
lecture / practicals
2 / 2
26 / 26
3
(Single degree study, master II. deg., full time form)
Z
2
doc. Mgr. Róbert Breier, PhD., doc. RNDr. Tibor Ženiš, PhD.
16.02.2026
20.06.2022
FMFI.KAMŠ/2-EFM-140/19
FMFI.KAMŠ/2-EFM-140/19 - SQL Databases
2-EFM-140 - SQL Databases
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
practicals
2
26
prezenčná
124585
FMFI.KAMŠ/2-EFM-140/19
2-EFM-140
SQL Databases
C
4374
mEMMa
Mathematical Economics, Finance and Modelling
Z
2
on-site learning
3
954
mPMS
Probability and Mathematical Statistics
Z
2
on-site learning
3
5620
mBMF
Biomedical Physics
Z
3
on-site learning
5
4387
mPMSa
Probability and Mathematical Statistics
Z
2
on-site learning
3
975
BMF
Biomedical Physics
Z
3
on-site learning
5
3965
mEMM
Mathematical Economics, Finance and Modelling
Z
2
on-site learning
3
I., I.II., II.
on-site learning
true
I., I.II., II.
1011351
1
P
lecturer
Róbert
Breier
doc. Mgr. Róbert Breier, PhD.
breier1@uniba.sk
Slovak
1005049
2
P
lecturer
Tibor
Ženiš
doc. RNDr. Tibor Ženiš, PhD.
zenis1@uniba.sk
Slovak
SK
Slovak
Slovak
Z
3
<_L_>
Recommended literature
Daniel Schneller, Udo Schwedt, MySQL Admin Cookbook, Packt Publishing Ltd. 2010
https://goalkicker.com/MySQLBook/MySQLNotesForProfessionals.pdf
Graeme Simsion, Graham Witt, Data Modeling Essentials, Elsevier 2004, ISBN: 9780080488677
<_PA_>
Conditions for completion of course
Preliminary semester evaluation: active participation
Final examination: oral, semestral project
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
Introduction to databases, database technologies, database models
Relational database model
SQL language
Relational databases
User rights in SQL
Database API
Interfaces for working with databases (web interface, CLI, GUI)
SQL programming (implementation in R, Matlab, Python, C ++)
<_VH_>
Weighting of course assessment (continuous/final)
Scale of assessment (preliminary/final): 20/80
<_VV_>
Learning outcomes
By completing the course, the student will gain the ability to work with databases, including database model design, creation in common SQL databases, and user interface programming in python, R, C ++, Matlab
A
94
56.63
B
20
12.05
C
16
9.64
D
17
10.24
E
10
6.02
FX
9
5.42
166
6
119224
B
2-EFM-143
FMFI.KAMŠ/2-EFM-143/17
Selected Actuarial Techniques
2
Evaluation
seminar
2
26
1, 3
(Single degree study, master II. deg., full time form)
Z
1, 2
Mgr. Matúš Džubák, Mgr. Gábor Szűcs, PhD.
16.02.2026
12.12.2025
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
seminar
2
26
prezenčná
C
3965
mEMM
Mathematical Economics, Finance and Modelling
Z
1
on-site learning
1
4374
mEMMa
Mathematical Economics, Finance and Modelling
Z
2
on-site learning
3
954
mPMS
Probability and Mathematical Statistics
Z
2
on-site learning
3
4374
mEMMa
Mathematical Economics, Finance and Modelling
Z
1
on-site learning
1
4387
mPMSa
Probability and Mathematical Statistics
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
1
on-site learning
1
954
mPMS
Probability and Mathematical Statistics
Z
1
on-site learning
1
3965
mEMM
Mathematical Economics, Finance and Modelling
Z
2
on-site learning
3
II.
on-site learning
true
II.
1068187
1
P
lecturer
Matúš
Džubák
Mgr. Matúš Džubák
dzubak1@uniba.sk
Slovak
1022694
2
P
lecturer
Gábor
Szűcs
Mgr. Gábor Szűcs, PhD.
szucs1@uniba.sk
Slovak
SK
Slovak
Slovak
Z
1
3
<_L_>
Recommended literature
Modern Actuarial Risk Theory Using R / Rob Kaas, Marc Goovaerts, Jan Dhaene, Michel Denuit. Heidelberg : Springer, 2008, ISBN: 978-3-540-70998-5;
Jazyk R v aktuárskych analýzach / Michal Páleš. Bratislava : Vydavateľstvo EKONÓM, 2017, ISBN 978-80-225-4331-6;
Jazyk R pre aktuárov / Michal Páleš. Bratislava : Vydavateľstvo EKONÓM, 2019, ISBN 978-80-225-4331-6;
Zurich Insurance Company Ltd internal training materials.
<_PA_>
Conditions for completion of course
During the semester, the student can get 100% of points, for solving individual assignments (50%) and elaboration and submission of the semester project (50%).
Grade thresholds: A: at least 90.00%; B: 89.99% – 80.00%; C: 79.99% – 70.00%; D: 69.99% – 60.00%; E: 59.99% – 50.00%; Fx: 49.99% – 0.00%.
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_PZ_>
Notes
Limit: maximum 18 students.
It is recommended to have basic R-software skills. At the seminars, we present quantitative techniques used by actuaries and use real data sets from the area of life and non-life insurance.
<_SO_>
Brief outline of the course
Actuarial models in life insurance. Modelling the development of insurance contracts from the perspective of the client. Development of the number of insurance contracts with respect to the expected probability of death and lapse. Development of the mathematical reserves in the entire portfolio. Modelling of other variables affecting the profit of the insurance company. Modelling the future profits using the direct method. Modelling the future profits using the indirect method. Modelling of the present value of financial indicators. Impact of changes in assumptions on the profit of an insurance company.
Data preparation for modelling using the software R. Application of Generalized Linear Models (GLM) using the software R – selecting of appropriate parameters, presentation of results using R Shiny package.
Portfolio management in the insurance company using actuarial and financial indicators and relationships between them.
<_VH_>
Weighting of course assessment (continuous/final)
Practical work during semester 100% / final exam 0%.
<_VV_>
Learning outcomes
After completing the course, the student will master basic methods of work in life and non-life insurance in an international insurance company, focusing on methods of cash-flow projection. The student will be able to calculate actuarial and financial indicators.
A
96
68.09
B
17
12.06
C
11
7.8
D
13
9.22
E
3
2.13
FX
1
0.71
141
6
120338
B
2-EFM-153
FMFI.KAMŠ/2-EFM-153/17
Finance and Insurance in Practice (1)
2
Evaluation
seminar
2
26
1, 3
(Single degree study, master II. deg., full time form)
Z
1, 2
16.02.2026
16.06.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
seminar
2
26
prezenčná
C
3965
mEMM
Mathematical Economics, Finance and Modelling
Z
1
on-site learning
1
4374
mEMMa
Mathematical Economics, Finance and Modelling
Z
2
on-site learning
3
954
mPMS
Probability and Mathematical Statistics
Z
2
on-site learning
3
4374
mEMMa
Mathematical Economics, Finance and Modelling
Z
1
on-site learning
1
4387
mPMSa
Probability and Mathematical Statistics
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
1
on-site learning
1
954
mPMS
Probability and Mathematical Statistics
Z
1
on-site learning
1
3965
mEMM
Mathematical Economics, Finance and Modelling
Z
2
on-site learning
3
II.
on-site learning
true
II.
1022694
1
V
seminar supervisor
Gábor
Szűcs
Mgr. Gábor Szűcs, PhD.
szucs1@uniba.sk
Slovak
SK
Slovak
Slovak
Z
1
3
<_L_>
Recommended literature
Poisťovníctvo / Anna Majtánová a kolektív. Bratislava : Wolters Kluwer (Iura Edition), 2009;
Lecturer's notes and handouts.
<_PA_>
Conditions for completion of course
During the semester the student can obtain 100% of points, for active participation in seminars (80%), solving tasks during seminars (20%), and possibly for additional individual practical work.
Grade thresholds: A: 100.00% – 90.00%; B: 89.99% – 80.00%; C: 79.99% – 70.00%; D: 69.99% – 60.00%; E: 59.99% – 50.00%; Fx: 49.99% – 0.00%.
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
Basic financial and insurance segments. Banks and insurance companies - joint-stock companies and their legislative background.
The role of financial analysts, risk managers and actuaries in banks, insurance companies and in other financial institutions. International professional qualification of actuaries, stages in actuarial qualification, the career of an actuary. Profession of Actuary in Slovakia and globally.
Professional software used in financial and insurance practice.
<_VH_>
Weighting of course assessment (continuous/final)
Practical work during semester 100% / final exam 0%.
<_VV_>
Learning outcomes
After completing the course, the student will master the basics of work in the financial and insurance business. Student should also learn about the roles of the actuary, risk manager and financial analyst.
A
55
78.57
B
11
15.71
C
1
1.43
D
1
1.43
E
1
1.43
FX
1
1.43
70
6
120339
B
2-EFM-154
FMFI.KAMŠ/2-EFM-154/17
Finance and Insurance in Practice (2)
2
Evaluation
seminar
2
26
2, 4
(Single degree study, master II. deg., full time form)
L
1, 2
16.02.2026
16.06.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
seminar
2
26
prezenčná
C
954
mPMS
Probability and Mathematical Statistics
L
2
on-site learning
4
4374
mEMMa
Mathematical Economics, Finance and Modelling
L
1
on-site learning
2
954
mPMS
Probability and Mathematical Statistics
L
1
on-site learning
2
3965
mEMM
Mathematical Economics, Finance and Modelling
L
2
on-site learning
4
4387
mPMSa
Probability and Mathematical Statistics
L
2
on-site learning
4
4387
mPMSa
Probability and Mathematical Statistics
L
1
on-site learning
2
3965
mEMM
Mathematical Economics, Finance and Modelling
L
1
on-site learning
2
4374
mEMMa
Mathematical Economics, Finance and Modelling
L
2
on-site learning
4
II.
on-site learning
true
II.
1022694
1
V
seminar supervisor
Gábor
Szűcs
Mgr. Gábor Szűcs, PhD.
szucs1@uniba.sk
Slovak
SK
Slovak
Slovak
L
2
4
<_L_>
Recommended literature
Poisťovníctvo / Anna Majtánová a kolektív. Bratislava : Wolters Kluwer (Iura Edition), 2009;
Lecturer's notes and handouts.
<_PA_>
Conditions for completion of course
During the semester the student can obtain 100% of points, for active participation in seminars (80%), solving tasks during seminars (20%), and possibly for additional individual practical work.
Grade thresholds: A: 100.00% – 90.00%; B: 89.99% – 80.00%; C: 79.99% – 70.00%; D: 69.99% – 60.00%; E: 59.99% – 50.00%; Fx: 49.99% – 0.00%.
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
Basic financial and insurance segments. Actuarial model development in life and non-life insurance. Financial modelling.
The role of financial analysts, risk managers and actuaries in banks, insurance companies and in other financial institutions. Professional requirements for financial analysts, risk managers and actuaries.
Professional software used in financial and insurance practice.
<_VH_>
Weighting of course assessment (continuous/final)
Practical work during semester 100% / final exam 0%.
<_VV_>
Learning outcomes
After completing the course, the student will master the basics of work in the financial and insurance business. Student should also learn about the roles of the actuary, risk manager and financial analyst.
A
41
75.93
B
6
11.11
C
5
9.26
D
1
1.85
E
0
0.0
FX
1
1.85
54
6
133638
B
2-EFM-155
FMFI.KAMŠ/2-EFM-155/22
Social Network Analysis
2
Evaluation
course
2
26
4
(Single degree study, master II. deg., full time form)
L
2
doc. RNDr. Beáta Stehlíková, PhD.
16.02.2026
17.06.2022
FMFI.KAMŠ/2-EFM-155/18
FMFI.KAMŠ/2-EFM-155/18 - Social Network Analysis
2-EFM-155 - Social Network Analysis
FMFI.KAMŠ/2-EFM-155/21
FMFI.KAMŠ/2-EFM-155/21 - Social Network Analysis
2-EFM-155 - Social Network Analysis
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
course
2
26
prezenčná
123678
FMFI.KAMŠ/2-EFM-155/18
2-EFM-155
Social Network Analysis
130812
FMFI.KAMŠ/2-EFM-155/21
2-EFM-155
Social Network Analysis
C
954
mPMS
Probability and Mathematical Statistics
L
2
on-site learning
4
3965
mEMM
Mathematical Economics, Finance and Modelling
L
2
on-site learning
4
4387
mPMSa
Probability and Mathematical Statistics
L
2
on-site learning
4
4374
mEMMa
Mathematical Economics, Finance and Modelling
L
2
on-site learning
4
II.
on-site learning
true
II.
1045125
1
P
lecturer
Beáta
Stehlíková
doc. RNDr. Beáta Stehlíková, PhD.
stehlikova1@uniba.sk
Slovak
SK
Slovak
Slovak
L
4
<_L_>
Recommended literature
KOLACZYK, Eric D.; CSÁRDI, Gábor. Statistical analysis of network data with R. New York: Springer, 2014.
LUKE, Douglas A. A user's guide to network analysis in R. London, England: Springer, 2015.
Journal papers.
<_O_>
Content prerequisite
Basics of R language
<_PA_>
Conditions for completion of course
Assessment during the term: homework (20%), project (80%).
Grading: A: 90 and more, B: [80, 90), C: [70, 80), D: [60, 70), E: [50, 60), FX: less than 50
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
Basic notions of graph theory, examples of graphs/networks, their visualization. Node centralities - classical centralities (degree, closeness, betweenness, eigenvector centrality), selected new centrality measures, applications. Detection of communities in networks - concept of modularity, modularity optimization, selected other methods, applications. Networks based on correlations between time series. Random networks and their basic properties.
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
Students gain an overview of different methods used in analysis of social networks. They will be able to apply them to real data using R software.
A
30
78.95
B
1
2.63
C
5
13.16
D
2
5.26
E
0
0.0
FX
0
0.0
38
6
133634
B
2-EFM-201
FMFI.KAMŠ/2-EFM-201/22
Insurance Theory
3
Evaluation
lecture
2
26
3
(Single degree study, master II. deg., full time form)
Z
2
Mgr. Gábor Szűcs, PhD., doc. Mgr. Igor Melicherčík, PhD.
16.02.2026
12.12.2025
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
C
4374
mEMMa
Mathematical Economics, Finance and Modelling
Z
2
on-site learning
3
954
mPMS
Probability and Mathematical Statistics
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
2
on-site learning
3
964
mMMN
Managerial Mathematics
Z
2
on-site learning
3
4382
mMMNa
Managerial Mathematics
Z
2
on-site learning
3
3965
mEMM
Mathematical Economics, Finance and Modelling
Z
2
on-site learning
3
II.
on-site learning
true
II.
1022694
1
P
lecturer
Gábor
Szűcs
Mgr. Gábor Szűcs, PhD.
szucs1@uniba.sk
Slovak
1009661
2
P
lecturer
Igor
Melicherčík
doc. Mgr. Igor Melicherčík, PhD.
melichercik2@uniba.sk
Slovak
SK
Slovak
Slovak
Z
3
<_L_>
Recommended literature
Life Insurance Mathematics / Hans U. Gerber. Heidelberg : Springer, 1997, ISBN 978-3-662-03460-6;
Modely v životnom a neživotnom poistení / Rastislav Potocký. Bratislava : Statis, 2012;
Modern Actuarial Risk Theory Using R / Rob Kaas, Marc Goovaerts, Jan Dhaene, Michel Denuit. Second Edition, Heidelberg : Springer-Verlag, 2008;
Poistná matematika / Viera Sekerová, Mária Bilíková, Bratislava : Ekonóm, 2005;
Aplikovaná poistná štatistika / Viera Pacáková. Bratislava : Iura Edition, 2004;
study materials of the lecturer.
<_PA_>
Conditions for completion of course
The course assessment is based only on the final written exam (with a weight of 100%), which has a possible (supplementary) oral part.
Grade thresholds: A: at least 90.00%; B: 89.99% – 80.00%; C: 79.99% – 70.00%; D: 69.99% – 60.00%; E: 59.99% – 50.00%; Fx: 49.99% – 0.00%.
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
The collective risk model in general insurance. Methods of mathematical modeling in life and non-life insurance, actuarial model construction techniques. Deductible, excess, and franchise. Reinsurance: proportional and non-proportional forms of reinsurance. Estimation of technical provisions in non-life insurance, deterministic and stochastic run-off triangles. The stochastic model of life insurance. Yield curves and their applications in insurance. International Financial Reporting Standard (IFRS) 17 Insurance Contracts and its impact on the actuarial calculations.
<_VH_>
Weighting of course assessment (continuous/final)
Practical work during semester 0% / final exam 100%.
<_VV_>
Learning outcomes
After completing the course, the student will master mathematical modeling techniques in life and non-life insurance. The student will be able to solve typical problems, e.g., risk premium calculation and estimation of the technical provisions. The student will also get acquainted with forms of deductible and reinsurance and actuarial modeling techniques.
A
27
65.85
B
3
7.32
C
8
19.51
D
2
4.88
E
1
2.44
FX
0
0.0
41
6
88286
B
2-EFM-217
FMFI.KAMŠ/2-EFM-217/11
Insurance Theory Classes
2
Evaluation
practicals
2
26
3
(Single degree study, master II. deg., full time form)
Z
2
Mgr. Gábor Szűcs, PhD., Mgr. Lívia Rosová, PhD.
16.02.2026
12.12.2025
FMFI.KAMŠ/2-PMS-217/10
FMFI.KAMŠ/2-PMS-217/10 - Insurance Theory Classes
2-PMS-217 - Insurance Theory Classes
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
practicals
2
26
prezenčná
81385
FMFI.KAMŠ/2-PMS-217/10
2-PMS-217
Insurance Theory Classes
C
4374
mEMMa
Mathematical Economics, Finance and Modelling
Z
2
on-site learning
3
954
mPMS
Probability and Mathematical Statistics
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
2
on-site learning
3
964
mMMN
Managerial Mathematics
Z
2
on-site learning
3
4382
mMMNa
Managerial Mathematics
Z
2
on-site learning
3
3965
mEMM
Mathematical Economics, Finance and Modelling
Z
2
on-site learning
3
II.
on-site learning
true
II.
1022694
1
C
instructor
Gábor
Szűcs
Mgr. Gábor Szűcs, PhD.
szucs1@uniba.sk
Slovak
1099572
2
C
instructor
Lívia
Rosová
Mgr. Lívia Rosová, PhD.
lessova8@uniba.sk
Slovak
SK
Slovak
Slovak
Z
3
<_L_>
Recommended literature
Life Insurance Mathematics / Hans U. Gerber. Heidelberg : Springer, 1997, ISBN 978-3-662-03460-6;
Modely v životnom a neživotnom poistení / Rastislav Potocký. Bratislava : Statis, 2012;
Modern Actuarial Risk Theory Using R / Rob Kaas, Marc Goovaerts, Jan Dhaene, Michel Denuit. Second Edition, Heidelberg : Springer-Verlag, 2008;
Aplikovaná poistná štatistika / Viera Pacáková. Bratislava : Iura Edition, 2004;
study materials of the lecturer.
<_O_>
Content prerequisite
2-EFM-201 Insurance Mathematics
<_PA_>
Conditions for completion of course
During the semester, the student can get 100% points after writing one test, which is usually written during the last two weeks of the semester.
Grade thresholds: A: at least 90.00%; B: 89.99% – 80.00%; C: 79.99% – 70.00%; D: 69.99% – 60.00%; E: 59.99% – 50.00%; Fx: 49.99% – 0.00%.
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
Methods of mathematical modeling in life and non-life insurance, actuarial model construction techniques. Deductible, excess, and franchise. Reinsurance: proportional and non-proportional forms of reinsurance. Estimation of technical provisions in non-life insurance, deterministic run-off triangles: chain-ladder method, separation method and other methods. The stochastic model of the life insurance. Yield curves and their applications in insurance.
<_VH_>
Weighting of course assessment (continuous/final)
Practical work during semester 100% / final exam 0%.
<_VV_>
Learning outcomes
After completing the course, the student will master mathematical modeling techniques in life and non-life insurance. The student will be able to solve typical problems, e.g., risk premium calculation and technical provision estimation. The student will also get acquainted with forms of deductible and reinsurance and actuarial modeling methods.
A
76
49.35
B
32
20.78
C
16
10.39
D
10
6.49
E
9
5.84
FX
11
7.14
154
6
104557
B
2-EFM-236
FMFI.KAMŠ/2-EFM-236/15
Modelling Biological Processes
3
Evaluation
lecture
2
26
3
(Single degree study, master II. deg., full time form)
Z
2
doc. Mgr. Pavol Bokes, PhD.
16.02.2026
19.10.2016
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
C
4374
mEMMa
Mathematical Economics, Finance and Modelling
Z
2
on-site learning
3
4373
mFBMa
Biomedical Physics
Z
2
on-site learning
3
954
mPMS
Probability and Mathematical Statistics
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
2
on-site learning
3
5620
mBMF
Biomedical Physics
Z
5
on-site learning
9
3965
mEMM
Mathematical Economics, Finance and Modelling
Z
2
on-site learning
3
976
mFBM
Biomedical Physics
Z
2
on-site learning
3
I.II., II.
on-site learning
true
I.II., II.
1079006
1
P
lecturer
Pavol
Bokes
doc. Mgr. Pavol Bokes, PhD.
bokes3@uniba.sk
English, Slovak
EN
English
SK
Slovak
English, Slovak
Z
3
<_L_>
Recommended literature
Mathematical biology : 1. : An introduction / J. D. Murray. New York : Springer, 2002
Mathematical biology : 2. : Spatial models and biomedical applications / J. D. Murray. New York : Springer, 2003
Keener, J., Sneyd, J., Mathematical physiology: I. Cellular physiology, 2nd. ed., Springer, New York, 2008
Wilkinson, D., Stochastic modelling for systems biology, 2nd ed., Chapman & Hall/CRC, Boca Raton, 2012.
<_PA_>
Conditions for completion of course
Continuous assessment: homework, exam during the semester
Exam: written and oral
Approximate grading scale: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
Biological modeling with ordinary differential equations: the principle of mass balance, mass action rule, scaling and nondimensionalisation, one-component models (Michaelis-Menten kinetics, gene autoregulation), multi-component models (biological switches, oscillators, epidemiology). Modeling with differential equations with delay. Models with spatial component: the reaction-diffusion systems, the spread of epidemics, pattern formation. Stochastic models: probability balance equation, Gillespie simulation algorithm, stochastic models of gene expression.
<_VV_>
Learning outcomes
Passing this subject, students will gain a basic understanding and overview of methods in biological modeling.
A
36
42.35
B
17
20.0
C
15
17.65
D
11
12.94
E
4
4.71
FX
2
2.35
85
6
31053
B
2-MXX-110
FMFI.KTV/2-MXX-110/00
Physical Education and Sport (1)
2
Evaluation
practicals
2
26
1, 7
(Single degree study, master II. deg., full time form)
Z
1, 4
PaedDr. Dana Mašlejová, Mgr. Ladislav Mókus, Mgr. Jana Leginusová, Mgr. Tomáš Kuchár, PhD., PaedDr. Mikuláš Ortutay, Mgr. Martin Dovičák, PhD., Mgr. Júlia Raábová, PhD., Mgr. Branislav Nedbálek, PhD., Mgr. Tomáš Lovecký, Mgr. Martina Maheľová, PaedDr. Lucia Ondrušová
16.02.2026
15.03.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
practicals
2
26
prezenčná
C
955
mFTF
Theoretical Physics
Z
4
on-site learning
7
968
mINF
Computer Science
Z
4
on-site learning
7
1018
muMAIN
Teaching Mathematics and Computer Science
Z
1
on-site learning
1
1019
mAIN
Applied Computer Science
Z
1
on-site learning
1
3006
muFYIN
Teaching Physics and Computer Science
Z
1
on-site learning
1
3507
muMADG/k
Teaching Mathematics and Descriptive Geometry (Conversion Programme)
Z
1
on-site learning
1
3510
muMAIN/k
Teaching Mathematics and Computer Science (Conversion Programme)
Z
1
on-site learning
1
3510
muMAIN/k
Teaching Mathematics and Computer Science (Conversion Programme)
Z
4
on-site learning
7
3965
mEMM
Mathematical Economics, Finance and Modelling
Z
1
on-site learning
1
4384
mFOSa
Optics, Lasers and Optical Spectroscopy
Z
4
on-site learning
7
4386
mMPGa/k
Computer Graphics and Geometry (Conversion Programme)
Z
1
on-site learning
1
5123
muINAN
Teaching Computer Science and English Language and Literature
Z
4
on-site learning
7
5688
mBIN
Bioinformatics
Z
1
on-site learning
1
956
mMPG
Computer Graphics and Geometry
Z
1
on-site learning
1
964
mMMN
Managerial Mathematics
Z
1
on-site learning
1
1018
muMAIN
Teaching Mathematics and Computer Science
Z
4
on-site learning
7
2979
mMAT
Mathematics
Z
4
on-site learning
7
2980
mFOS
Optics, Lasers and Optical Spectroscopy
Z
4
on-site learning
7
4374
mEMMa
Mathematical Economics, Finance and Modelling
Z
4
on-site learning
7
4375
mEOMa
Environmental Physics, Renewable Energy Sources and Meteorology and Climatology
Z
4
on-site learning
7
4381
mIKVa
Cognitive Science
Z
4
on-site learning
7
4383
mMATa
Mathematics
Z
4
on-site learning
7
4384
mFOSa
Optics, Lasers and Optical Spectroscopy
Z
1
on-site learning
1
5574
muINGE
Teaching Computer Science and Geography
Z
1
on-site learning
1
5620
mBMF
Biomedical Physics
Z
4
on-site learning
7
5687
mDAV
Data Science
Z
1
on-site learning
1
965
mINF/k
Computer Science (Conversion Programme)
Z
1
on-site learning
1
978
mFAA
Astronomy and Astrophysics
Z
4
on-site learning
7
2980
mFOS
Optics, Lasers and Optical Spectroscopy
Z
1
on-site learning
1
3006
muFYIN
Teaching Physics and Computer Science
Z
4
on-site learning
7
3007
muINBI
Teaching Computer Science and Biology
Z
4
on-site learning
7
3966
mEOM
Environmental Physics, Renewable Energy Sources and Meteorology and Climatology
Z
4
on-site learning
7
3971
mFPE
Physics of the Earth
Z
4
on-site learning
7
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1005342
1
C
instructor
Dana
Mašlejová
PaedDr. Dana Mašlejová
maslejova1@uniba.sk
Slovak
1009061
2
C
instructor
Ladislav
Mókus
Mgr. Ladislav Mókus
mokus2@uniba.sk
Slovak
1045838
3
C
instructor
Jana
Leginusová
Mgr. Jana Leginusová
leginusova1@uniba.sk
Slovak
1049011
4
C
instructor
Tomáš
Kuchár
Mgr. Tomáš Kuchár, PhD.
kuchar1@uniba.sk
Slovak
1045754
5
C
instructor
Mikuláš
Ortutay
PaedDr. Mikuláš Ortutay
ortutay1@uniba.sk
Slovak
1003472
6
C
instructor
Martin
Dovičák
Mgr. Martin Dovičák, PhD.
dovicak1@uniba.sk
Slovak
1017460
7
C
instructor
Júlia
Raábová
Mgr. Júlia Raábová, PhD.
raabova1@uniba.sk
Slovak
1230273
8
C
instructor
Branislav
Nedbálek
Mgr. Branislav Nedbálek, PhD.
nedbalek2@uniba.sk
Slovak
1051507
9
C
instructor
Tomáš
Lovecký
Mgr. Tomáš Lovecký
lovecky3@uniba.sk
Slovak
1060552
10
C
instructor
Martina
Maheľová
Mgr. Martina Maheľová
Slovak
1367237
11
P
lecturer
Lucia
Ondrušová
PaedDr. Lucia Ondrušová
Slovak
SK
Slovak
Slovak
Z
1
7
<_C_>
Goal
Basic practicing and improvement of locomotion skills in various selected sports: aerobics, basketball, volleyball, football, floorball, hockey, badminton, tennis, table tennis, swimming, body building, trampoline jumping, paddling. Development of a harmonic personality in the sense of “kalokagatia” – to cultivate the students´ mental and physical values.
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
Practicing of the students´ game skills in collective sports: basketball, volleyball, football, floorball and hockey. Mastering of the basic technique of a particular sport discipline in other sports. In paddling, basic training on still and slightly flowing water. Development of coordination skills, improvement of articular mobility and cardiovascular system.
<_VH_>
Weighting of course assessment (continuous/final)
100/0
A
1955
97.41
B
12
0.6
C
2
0.1
D
0
0.0
E
0
0.0
FX
38
1.89
2007
6
118709
B
2-MXX-115
FMFI.KTV/2-MXX-115/17
Sports in Natur (1)
2
Evaluation
1, 7
(Single degree study, master II. deg., full time form)
Z
1, 4
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16.02.2026
16.06.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
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1003472
1
C
instructor
Martin
Dovičák
Mgr. Martin Dovičák, PhD.
dovicak1@uniba.sk
Slovak
1049011
2
P
lecturer
Tomáš
Kuchár
Mgr. Tomáš Kuchár, PhD.
kuchar1@uniba.sk
Slovak
1045838
3
C
instructor
Jana
Leginusová
Mgr. Jana Leginusová
leginusova1@uniba.sk
Slovak
1005342
4
C
instructor
Dana
Mašlejová
PaedDr. Dana Mašlejová
maslejova1@uniba.sk
Slovak
1009061
5
C
instructor
Ladislav
Mókus
Mgr. Ladislav Mókus
mokus2@uniba.sk
Slovak
1045754
6
C
instructor
Mikuláš
Ortutay
PaedDr. Mikuláš Ortutay
ortutay1@uniba.sk
Slovak
1017460
7
C
instructor
Júlia
Raábová
Mgr. Júlia Raábová, PhD.
raabova1@uniba.sk
Slovak
1051507
8
C
instructor
Tomáš
Lovecký
Mgr. Tomáš Lovecký
lovecky3@uniba.sk
Slovak
SK
Slovak
Slovak
Z
1
7
<_PA_>
Conditions for completion of course
Grades: A 90%, B 80%, C 70%, D 60%, E 50%
The condition for the award of 1 or 2 credits is the completion of a multi-day course in its full scope, or the completion of one-day courses in the scope of 4 days. Candidates can apply to the leaders of individual courses. From the presented offer of courses, you can choose the one that suits your interests, abilities and deadlines.
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak
<_PZ_>
Notes
KTVŠ does not rent ski equipment.
<_SO_>
Brief outline of the course
The student can sign up for the outdoor sports courses offered by the department: skiing, snowboarding. The lessons in the courses are focused on the development of basic and special movement skills and mastering the techniques needed for the sports.
<_VV_>
Learning outcomes
Acquisition and development of basic motor skills and abilities in selected sports: skiing and snowboarding. Mastering the correct technique of performing individual movements, which are necessary for skiing and snowboarding.
A
184
98.92
B
0
0.0
C
0
0.0
D
0
0.0
E
0
0.0
FX
2
1.08
186
6
122252
B
2-MXX-116
FMFI.KTV/2-MXX-116/18
Sports in Natur (2)
2
Evaluation
2, 8
(Single degree study, master II. deg., full time form)
L
1, 4
Mgr. Martin Dovičák, PhD., Mgr. Tomáš Kuchár, PhD., Mgr. Jana Leginusová, PaedDr. Dana Mašlejová, Mgr. Ladislav Mókus, PaedDr. Mikuláš Ortutay, Mgr. Júlia Raábová, PhD., Mgr. Tomáš Lovecký
16.02.2026
16.06.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
C
955
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4
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4
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L
1
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2
I.II., II.
on-site learning
true
1003472
1
C
instructor
Martin
Dovičák
Mgr. Martin Dovičák, PhD.
dovicak1@uniba.sk
Slovak
1049011
2
P
lecturer
Tomáš
Kuchár
Mgr. Tomáš Kuchár, PhD.
kuchar1@uniba.sk
Slovak
1045838
3
C
instructor
Jana
Leginusová
Mgr. Jana Leginusová
leginusova1@uniba.sk
Slovak
1005342
4
C
instructor
Dana
Mašlejová
PaedDr. Dana Mašlejová
maslejova1@uniba.sk
Slovak
1009061
5
C
instructor
Ladislav
Mókus
Mgr. Ladislav Mókus
mokus2@uniba.sk
Slovak
1045754
6
C
instructor
Mikuláš
Ortutay
PaedDr. Mikuláš Ortutay
ortutay1@uniba.sk
Slovak
1017460
7
C
instructor
Júlia
Raábová
Mgr. Júlia Raábová, PhD.
raabova1@uniba.sk
Slovak
1051507
8
C
instructor
Tomáš
Lovecký
Mgr. Tomáš Lovecký
lovecky3@uniba.sk
Slovak
SK
Slovak
Slovak
L
2
8
<_PA_>
Conditions for completion of course
Grades: A 90%, B 80%, C 70%, D 60%, E 50%.
The condition for the award of 1 or 2 credits is the completion of a multi-day course in its full scope, or the completion of one-day courses in the scope of 4 days. Candidates can apply to the leaders of individual courses. From the presented offer of courses, you can choose the one that suits your interests, abilities and deadlines.
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak
<_PZ_>
Notes
KTVŠ will provide sports equipment.
<_SO_>
Brief outline of the course
The student can sign up for the outdoor sports courses offered by the department: water tourism - river rafting, windsurfing, beach volleyball, hiking and other hobby sports. The lessons in the courses are focused on the development of basic and special movement skills and, mastering the techniques needed for the sports.
<_VV_>
Learning outcomes
Creating a positive and lasting relationship with physical activity. Acquisition and mastery of basic motor skills and abilities in outdoor sports: windsurfing, beach volleyball, water tourism - river rafting, hiking and other sports according to interest. Training and improving the technique needed for the sports.
A
104
95.41
B
0
0.0
C
0
0.0
D
0
0.0
E
0
0.0
FX
5
4.59
109
6
130637
B
2-MXX-130
FMFI.KAI/2-MXX-130/21
Elements of AI
2
Evaluation
independent work
25
325
1, 7
(Single degree study, master II. deg., full time form)
Z
1, 4
doc. RNDr. Mária Markošová, PhD.
16.02.2026
22.08.2021
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
independent work
25
325
prezenčná
C
955
mFTF
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4
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4
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1
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4
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on-site learning
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1104
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1
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Z
1
on-site learning
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4378
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4
on-site learning
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4380
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Z
1
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4
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Z
4
on-site learning
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on-site learning
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1
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Z
1
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1
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Z
1
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Z
1
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4
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Z
1
on-site learning
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Z
1
on-site learning
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Probability and Mathematical Statistics
Z
4
on-site learning
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Z
1
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Z
1
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4
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Applied Computer Science
Z
1
on-site learning
1
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Z
1
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Z
1
on-site learning
1
5574
muINGE
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Z
4
on-site learning
7
956
mMPG
Computer Graphics and Geometry
Z
4
on-site learning
7
968
mINF
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Z
1
on-site learning
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970
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Z
4
on-site learning
7
978
mFAA
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Z
1
on-site learning
1
1017
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Z
4
on-site learning
7
2596
mAIN/k
Applied Computer Science (Conversion Programme)
Z
4
on-site learning
7
3007
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Z
1
on-site learning
1
3507
muMADG/k
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Z
4
on-site learning
7
3509
muMAFY/k
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Z
4
on-site learning
7
3971
mFPE
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Z
1
on-site learning
1
4377
mFTLa
Solid State Physics
Z
4
on-site learning
7
4380
mFJFa
Nuclear and Sub-nuclear Physics
Z
4
on-site learning
7
4388
mFTFa
Theoretical Physics
Z
1
on-site learning
1
4388
mFTFa
Theoretical Physics
Z
4
on-site learning
7
5687
mDAV
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Z
4
on-site learning
7
I.II., II.
on-site learning
true
II.
1020529
1
P
lecturer
Mária
Markošová
doc. RNDr. Mária Markošová, PhD.
markosova1@uniba.sk
Slovak
SK
Slovak
Slovak
Z
1
7
<_L_>
Recommended literature
Russell S., Norwig P. (2010). Artificial Intelligence: A Modern Approach, (3rd ed.), Prentice Hall. Available in faculty library.
Marsland S. (2015). Machine Learning: An Algorithmic Perspective, (2nd ed.), CRC Press.
<_PA_>
Conditions for completion of course
Passing the online course https://course.elementsofai.com/ (in Enlish or Slovak version).
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak or English
<_PZ_>
Notes
The course consists of 20 numerical and 5 text-based tasks. Numerical tasks are checked automatically, text-based tasks are evaluated anonymously by students.
<_SO_>
Brief outline of the course
1. What is artificial intelligence: related areas, AI philosophy.
2. Troubleshooting and UI: Browsing and troubleshooting, browsing and games
3. Probability and chance, Bayes' theorem, naive Bayesian classification.
4. Machine learning: nearest neighbor classifier, regression.
5. Neural networks: basics, creation, modern techniques.
6. Consequences: on predicting the future, the effects of AI on society, summary.
<_VV_>
Learning outcomes
The student will get acquainted with selected basic concepts of artificial intelligence and their use in solving various practical tasks.
A
95
100.0
B
0
0.0
C
0
0.0
D
0
0.0
E
0
0.0
FX
0
0.0
95
6
130637
B
2-MXX-130
FMFI.KAI/2-MXX-130/21
Elements of AI
2
Evaluation
independent work
25
325
2, 8
(Single degree study, master II. deg., full time form)
L
1, 4
doc. RNDr. Mária Markošová, PhD., prof. Ing. Igor Farkaš, Dr., doc. RNDr. Martin Takáč, PhD.
16.02.2026
22.08.2021
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
independent work
25
325
prezenčná
C
955
mFTF
Theoretical Physics
L
4
on-site learning
8
965
mINF/k
Computer Science (Conversion Programme)
L
4
on-site learning
8
2604
muMATV
Teaching Mathematics and Physical Education
L
4
on-site learning
8
3507
muMADG/k
Teaching Mathematics and Descriptive Geometry (Conversion Programme)
L
1
on-site learning
2
4375
mEOMa
Environmental Physics, Renewable Energy Sources and Meteorology and Climatology
L
1
on-site learning
2
4375
mEOMa
Environmental Physics, Renewable Energy Sources and Meteorology and Climatology
L
4
on-site learning
8
4376
mFFPa
Plasma Physics
L
1
on-site learning
2
4378
mINFa
Computer Science
L
1
on-site learning
2
4385
mMPGa
Computer Graphics and Geometry
L
1
on-site learning
2
4387
mPMSa
Probability and Mathematical Statistics
L
4
on-site learning
8
5687
mDAV
Data Science
L
1
on-site learning
2
5687
mDAV
Data Science
L
4
on-site learning
8
954
mPMS
Probability and Mathematical Statistics
L
4
on-site learning
8
1019
mAIN
Applied Computer Science
L
4
on-site learning
8
2980
mFOS
Optics, Lasers and Optical Spectroscopy
L
4
on-site learning
8
3007
muINBI
Teaching Computer Science and Biology
L
1
on-site learning
2
4374
mEMMa
Mathematical Economics, Finance and Modelling
L
4
on-site learning
8
4381
mIKVa
Cognitive Science
L
1
on-site learning
2
4383
mMATa
Mathematics
L
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1
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I.II., II.
on-site learning
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II.
1020529
1
P
lecturer
Mária
Markošová
doc. RNDr. Mária Markošová, PhD.
markosova1@uniba.sk
Slovak
1029976
2
P
lecturer
Igor
Farkaš
prof. Ing. Igor Farkaš, Dr.
farkas3@uniba.sk
Slovak
1045892
3
P
lecturer
Martin
Takáč
doc. RNDr. Martin Takáč, PhD.
takac1@uniba.sk
Slovak
SK
Slovak
Slovak
L
2
8
<_L_>
Recommended literature
Russell S., Norwig P. (2010). Artificial Intelligence: A Modern Approach, (3rd ed.), Prentice Hall. Available in faculty library.
Marsland S. (2015). Machine Learning: An Algorithmic Perspective, (2nd ed.), CRC Press.
<_PA_>
Conditions for completion of course
Passing the online course https://course.elementsofai.com/ (in Enlish or Slovak version).
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak or English
<_PZ_>
Notes
The course consists of 20 numerical and 5 text-based tasks. Numerical tasks are checked automatically, text-based tasks are evaluated anonymously by students.
<_SO_>
Brief outline of the course
1. What is artificial intelligence: related areas, AI philosophy.
2. Troubleshooting and UI: Browsing and troubleshooting, browsing and games
3. Probability and chance, Bayes' theorem, naive Bayesian classification.
4. Machine learning: nearest neighbor classifier, regression.
5. Neural networks: basics, creation, modern techniques.
6. Consequences: on predicting the future, the effects of AI on society, summary.
<_VV_>
Learning outcomes
The student will get acquainted with selected basic concepts of artificial intelligence and their use in solving various practical tasks.
A
95
100.0
B
0
0.0
C
0
0.0
D
0
0.0
E
0
0.0
FX
0
0.0
95
6
130832
B
2-MXX-131
FMFI.KDMFI+KAI/2-MXX-131/21
International Team-based Research Project
5
Evaluation
course / independent work
3
39 / 30s
1, 7
(Single degree study, master II. deg., full time form)
Z
1, 4
prof. RNDr. Zuzana Kubincová, PhD., doc. RNDr. Martin Homola, PhD.
16.02.2026
22.06.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
course
3
39
prezenčná
independent work
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on-site learning
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4
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4377
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1
on-site learning
1
4378
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4
on-site learning
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4380
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1
on-site learning
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1
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1
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1
on-site learning
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1
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Probability and Mathematical Statistics
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1
on-site learning
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4
on-site learning
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955
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1
on-site learning
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1
on-site learning
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1019
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4
on-site learning
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4371
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Applied Computer Science
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1
on-site learning
1
4373
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1
on-site learning
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4382
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Managerial Mathematics
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1
on-site learning
1
5574
muINGE
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Z
4
on-site learning
7
3007
muINBI
Teaching Computer Science and Biology
Z
1
on-site learning
1
968
mINF
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Z
1
on-site learning
1
956
mMPG
Computer Graphics and Geometry
Z
4
on-site learning
7
970
mFFP
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Z
4
on-site learning
7
978
mFAA
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Z
1
on-site learning
1
1017
muMADG
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4
on-site learning
7
2596
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4
on-site learning
7
3507
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Z
4
on-site learning
7
3509
muMAFY/k
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Z
4
on-site learning
7
3971
mFPE
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Z
1
on-site learning
1
4377
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Solid State Physics
Z
4
on-site learning
7
4380
mFJFa
Nuclear and Sub-nuclear Physics
Z
4
on-site learning
7
4388
mFTFa
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Z
1
on-site learning
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4388
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4
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5687
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4
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I.II., II.
on-site learning
true
II.
1033993
1
P
lecturer
Zuzana
Kubincová
prof. RNDr. Zuzana Kubincová, PhD.
kubincova1@uniba.sk
English, Slovak
1033776
2
P
lecturer
Martin
Homola
doc. RNDr. Martin Homola, PhD.
homola1@uniba.sk
English, Slovak
EN
English
SK
Slovak
English, Slovak
Z
1
7
<_L_>
Recommended literature
- Teachers' own electronic study materials published on the course website or in the Moodle system
- Gavora, Peter a kol. 2010. Elektronická učebnica pedagogického výskumu. [online]. Bratislava : Univerzita Komenského, 2010. Dostupné na: http://www.e-metodologia.fedu.uniba.sk/ ISBN 978–80–223–2951–4.
- Tharenou, P., Donohue, R. and Cooper, B., 2007. Management research methods. Cambridge University Press.
- Topping, A., 2015: The Quantitative-Qualitative Continium. In: Gerrish, K. and Lathlean, J., The Research Process in Nursing, p. 159-172
- Williamson, K. and Johanson, G. eds., 2017. Research methods: Information, systems, and contexts. Chandos Publishing.
<_PA_>
Conditions for completion of course
Continuous assessment: active participation in research in an international student team (25%), presentation of work in a workshop (25%), scientific article (50%)
Indicative evaluation scale: A 90 %, B 80 %, C 70 %, D 60 %, E 50 %
<_PJ_>
Language, which knowledge is needed to pass the course
English
<_SO_>
Brief outline of the course
- Research methodology
- Design and implementation of a research project in an international group (preferably interdisciplinary)
- Methods and tools for collaboration in virtual space, collaboration in science and practice
- Academic writing, presentation of research results through scientific articles; objectives, content and structure of scientific articles; forms of academic publication, publication forums and evaluation of their quality
- Quality assurance and feedback - peer review
- Communication of results through posters or conference presentations
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
Students will learn in the team to agree on a common research topic, formulate research questions, determine research methods for the problem, collect and evaluate data, discuss their findings, present research results to the professional public, analyze and evaluate the scientific work of their colleagues, prepare a scientific article suitable for publication
A
7
70.0
B
0
0.0
C
0
0.0
D
0
0.0
E
3
30.0
FX
0
0.0
10
6
156729
B
2-MXX-134
FMFI.CENAM/2-MXX-134/26
Innovation and Entrepreneurship in Natural and Technical Sciences
3
Evaluation
lecture / independent work
2 / 1
26 / 13
1, 7
(Single degree study, master II. deg., full time form)
Z
1, 4
doc. RNDr. Tomáš Plecenik, PhD., Mgr. Veronika Hidaši Turiničová, PhD.
25.02.2026
13.03.2026
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
independent work
1
13
prezenčná
C
955
mFTF
Theoretical Physics
Z
4
on-site learning
7
968
mINF
Computer Science
Z
4
on-site learning
7
1018
muMAIN
Teaching Mathematics and Computer Science
Z
1
on-site learning
1
1019
mAIN
Applied Computer Science
Z
1
on-site learning
1
3006
muFYIN
Teaching Physics and Computer Science
Z
1
on-site learning
1
3507
muMADG/k
Teaching Mathematics and Descriptive Geometry (Conversion Programme)
Z
1
on-site learning
1
3510
muMAIN/k
Teaching Mathematics and Computer Science (Conversion Programme)
Z
1
on-site learning
1
3510
muMAIN/k
Teaching Mathematics and Computer Science (Conversion Programme)
Z
4
on-site learning
7
3965
mEMM
Mathematical Economics, Finance and Modelling
Z
1
on-site learning
1
4384
mFOSa
Optics, Lasers and Optical Spectroscopy
Z
4
on-site learning
7
4386
mMPGa/k
Computer Graphics and Geometry (Conversion Programme)
Z
1
on-site learning
1
5123
muINAN
Teaching Computer Science and English Language and Literature
Z
4
on-site learning
7
5688
mBIN
Bioinformatics
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1
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1029748
1
P
lecturer
Tomáš
Plecenik
doc. RNDr. Tomáš Plecenik, PhD.
plecenik2@uniba.sk
Slovak
1220569
2
P
lecturer
Veronika
Hidaši Turiničová
Mgr. Veronika Hidaši Turiničová, PhD.
turinicova8@uniba.sk
Slovak
SK
Slovak
Slovak
Z
1
7
<_DI_>
Type, volume, methods and workload of the student - additional information
2/1 (lecture / individual work)
<_L_>
Recommended literature
Clark, Timothy R., et al. Business Model Generation. Wiley, 2010
<_PA_>
Conditions for completion of course
The condition for admission to the exam is active participation in at least 80% of the lessons.
The final assessment consists of a presentation of the semester project. To successfully complete the course, it is necessary to achieve at least 50% of the overall score.
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak
<_SO_>
Brief outline of the course
1. Commercialization of scientific research.
2. Fundamentals of entrepreneurship and startup terminology.
3. Identification of problems and customer needs analysis (design thinking).
4. Technology transfer. Technology Readiness Levels (TRL).
5. Intellectual property and its protection.
6. Market, customer, and market potential of a technological solution.
7. Business Model Canvas. Revenue models.
8. Sources of financing for technological projects.
9. Pitching and communication of the solution.
10. Fundamentals of management and leadership.
11. Innovation support and incubation structures at national and international levels.
<_VH_>
Weighting of course assessment (continuous/final)
0/100
<_VV_>
Learning outcomes
After completing the course, students can describe the possibilities for commercialization of scientific and technological research. They can identify market needs, assess the market potential of a technological solution, and are familiar with the terminology of entrepreneurship, technology transfer, and intellectual property protection. They understand the overall structure of a business plan and the main forms of financing for technological projects. They are familiar with the principles of communication, teamwork, and team leadership and can apply them appropriately in project work and its presentation.
A
0
0.0
B
0
0.0
C
0
0.0
D
0
0.0
E
0
0.0
FX
0
0.0
0
6
133640
B
2-PMS-101
FMFI.KAMŠ/2-PMS-101/22
Random Processes
3
Evaluation
lecture
2
26
1
(Single degree study, master II. deg., full time form)
Z
1
Mgr. Jozef Kováč, PhD., doc. RNDr. Beáta Stehlíková, PhD.
16.02.2026
22.06.2022
FMFI.KAMŠ/2-PMS-101/00
FMFI.KAMŠ/2-PMS-101/00 - Random Processes (1)
2-PMS-101 - Random Processes (1)
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
29254
FMFI.KAMŠ/2-PMS-101/00
2-PMS-101
Random Processes (1)
A
4387
mPMSa
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Z
1
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1
954
mPMS
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Z
1
on-site learning
1
II.
on-site learning
true
II.
1058242
1
P
lecturer
Jozef
Kováč
Mgr. Jozef Kováč, PhD.
kovac59@uniba.sk
Slovak
1045125
2
P
lecturer
Beáta
Stehlíková
doc. RNDr. Beáta Stehlíková, PhD.
stehlikova1@uniba.sk
Slovak
SK
Slovak
Slovak
Z
1
<_C_>
Goal
Students will acquire knowledge of the basic theory of stochastic processes, spectral analysis and selected methods for analyzing and predicting real world processes.
<_L_>
Recommended literature
Shumway R. H., Stoffer D.S.: Time Series Analysis and Its Applications With R Examples, New York: Springer 2011; Štulajter F.: Random processes and time series, Bratislava: Univerzita Komenského 2001; Hyndman R.J., Athanasopoulos, G.: Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia 2018
<_P_>
Continuous assessment
exam
<_PA_>
Conditions for completion of course
Preliminary semester evaluation: exam (20%), homework (40%);
Examination: test and oral examination (40%);
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_PZ_>
Notes
Enrollment limit: 25 students
<_SO_>
Brief outline of the course
probability distribution of stochastic process, expected value and covariance function of stochastic process, stationary stochastic process, spectral analysis, spectral density, its nonparametric and parametric estimation, time series decomposition, Holt-Winters method, time series regression.
<_VH_>
Weighting of course assessment (continuous/final)
60/40
<_VV_>
Learning outcomes
Students will acquire knowledge of the basic theory of stochastic processes, spectral analysis and selected methods for analyzing and predicting real world processes.
<_Z_>
Final assessment
examination
A
82
34.89
B
58
24.68
C
47
20.0
D
29
12.34
E
18
7.66
FX
1
0.43
235
6
133644
B
2-PMS-102
FMFI.KAMŠ/2-PMS-102/22
Time Series
3
Evaluation
course
3
39
1
(Single degree study, master II. deg., full time form)
Z
1
doc. RNDr. Beáta Stehlíková, PhD., Mgr. Anna Hlubinová
16.02.2026
24.08.2023
FMFI.KAMŠ/2-EFM-102/15 and FMFI.KAMS/2-INF-192/22 and FMFI.KAMŠ/2-INF-191/22
FMFI.KAMŠ/2-EFM-102/15 - Time Series Analysis and FMFI.KAMS/2-INF-192/22 - Practicum in Time Series and FMFI.KAMŠ/2-INF-191/22 - Time Series Analysis
2-EFM-102 - Time Series Analysis and 2-INF-192 - Practicum in Time Series and 2-INF-191 - Time Series Analysis
FMFI.KAMŠ/2-PMS-102/00
FMFI.KAMŠ/2-PMS-102/00 - Random Processes (2)
2-PMS-102 - Random Processes (2)
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
course
3
39
prezenčná
104541
FMFI.KAMŠ/2-EFM-102/15
2-EFM-102
Time Series Analysis
and
134159
FMFI.KAMS/2-INF-192/22
2-INF-192
Practicum in Time Series
and
134156
FMFI.KAMŠ/2-INF-191/22
2-INF-191
Time Series Analysis
29256
FMFI.KAMŠ/2-PMS-102/00
2-PMS-102
Random Processes (2)
A
4387
mPMSa
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Z
1
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1
954
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Z
1
on-site learning
1
II.
on-site learning
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II.
1045125
1
P
lecturer
Beáta
Stehlíková
doc. RNDr. Beáta Stehlíková, PhD.
stehlikova1@uniba.sk
Slovak
1289622
2
C
instructor
Anna
Hlubinová
Mgr. Anna Hlubinová
hlubinova9@uniba.sk
Slovak
SK
Slovak
Slovak
Z
1
<_L_>
Recommended literature
Introduction to modern time series analysis / Gebhard Kirchgässner, Jürgen Wolters. Berlin : Springer, 2008;
Time series analysis and its applications : with R examples / Robert H. Shumway, David S. Stoffer. New York : Springer, 2011.
<_O_>
Content prerequisite
Basics of statistics (linear regression, analysis of residuals, testing hypotheses) and use of R language, complex numbers
<_PA_>
Conditions for completion of course
Evaluation based on: homework (100%).
Grading: A: 90 and more, B: [80, 90), C: [70, 80), D: [60, 70), E: [50, 60), FX: less than 50
The weights of assessment (teaching period / examination period): 100/0
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
Stationarity of time series. White noise. Wold representation. Autocorrelation function. Testing white noise, Ljung-Boxing Q-statistics. Autoregressive models (AR), moving average models (MA), ARMA models. Stationarity and invertibility conditions. Calculation of expected value, dispersion and covariances. Autocorrelation and partial autocorrelation function and their use in identification of the model. Differentiation of time series, integrated processes. Unit root testing. ADF test. Seasonality, SARIMA models.
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
Students get familiar with Box-Jenkins methodology of time series modeling using ARIMA processes. They will know theoretical properties of these models and will be able to use them to analyze real data.
A
87
36.71
B
55
23.21
C
43
18.14
D
34
14.35
E
15
6.33
FX
3
1.27
237
6
104878
B
2-PMS-107
FMFI.KAMŠ/2-PMS-107/15
Regression Models
5
Evaluation
lecture / practicals
2 / 2
26 / 26
1
(Single degree study, master II. deg., full time form)
Z
1
Mgr. Samuel Rosa, PhD., prof. Mgr. Radoslav Harman, PhD.
16.02.2026
21.06.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
practicals
2
26
prezenčná
A
4387
mPMSa
Probability and Mathematical Statistics
Z
1
on-site learning
1
954
mPMS
Probability and Mathematical Statistics
Z
1
on-site learning
1
II.
on-site learning
true
II.
1046621
1
P
lecturer
Samuel
Rosa
Mgr. Samuel Rosa, PhD.
rosa2@uniba.sk
Slovak
1018338
2
P
lecturer
Radoslav
Harman
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
Slovak
SK
Slovak
Slovak
Z
1
<_L_>
Recommended literature
Pázman A, Lacko V: Prednášky z regresných modelov : Odhadovanie parametrov strednej hodnoty a štatistická optimalizácia experimentu, Univerzita Komenského, 2012;
Faraway J J: Extending the Linear Models with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, 2nd ed., Chapman & Hall/CRC, 2016
<_PA_>
Conditions for completion of course
Evaluation based on: test, project (teaching period), oral exam
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_PZ_>
Notes
Knowledge of linear regression is assumed, for instance at the level of Econometry 1-EFM-380.
<_SO_>
Brief outline of the course
1. Linear regression model – least squares estimator, rank-deficient model, Gauss-Markov theorem, confidence regions.
2. Generalized linear model – exponential class of distributions, link function, parameter estimation, hypothesis testing.
3. Nonlinear regression model – least squares estimator (LSE), properties of LSE and the computation of LSE, tests and confidence regions.
<_URL_>
Course web page URL
http://www.iam.fmph.uniba.sk/ospm/Rosa/teaching.htm
<_VH_>
Weighting of course assessment (continuous/final)
50 (test 30 + project 20)/50
<_VV_>
Learning outcomes
The students will acquire understanding of linear, generalized linear and nonlinear regression models. They will also learn how to apply these models to practical problems.
A
43
32.33
B
27
20.3
C
29
21.8
D
22
16.54
E
11
8.27
FX
1
0.75
133
6
124584
B
2-PMS-108
FMFI.KAMŠ/2-PMS-108/19
Design of experiments
5
Evaluation
lecture / practicals
2 / 2
26 / 26
2
(Single degree study, master II. deg., full time form)
L
1
doc. Mgr. Lenka Filová, PhD., prof. Mgr. Radoslav Harman, PhD.
16.02.2026
03.06.2025
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
practicals
2
26
prezenčná
A
4374
mEMMa
Mathematical Economics, Finance and Modelling
L
1
on-site learning
2
954
mPMS
Probability and Mathematical Statistics
L
1
on-site learning
2
3965
mEMM
Mathematical Economics, Finance and Modelling
L
2
on-site learning
4
4387
mPMSa
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L
1
on-site learning
2
3965
mEMM
Mathematical Economics, Finance and Modelling
L
1
on-site learning
2
4374
mEMMa
Mathematical Economics, Finance and Modelling
L
2
on-site learning
4
II.
on-site learning
true
II.
1014351
1
P
lecturer
Lenka
Filová
doc. Mgr. Lenka Filová, PhD.
filova2@uniba.sk
Slovak
1018338
2
P
lecturer
Radoslav
Harman
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
Slovak
SK
Slovak
Slovak
L
2
<_L_>
Recommended literature
Dean A, Voss D, Draguljic D: Design and Analysis of Experiments (Springer Texts in Statistics) 2nd ed., Springer 2017; Pázman A, Lacko V: Prednášky z regresných modelov - odhadovanie parametrov strednej hodnoty a štatistická optimalizácia experimentu, Univerzita Komenského 2012; A. Atkinson et al.: Optimum Experimental Design, with SAS. Oxford University Press 2007
<_O_>
Content prerequisite
Regression models 2-PMS-107
<_PA_>
Conditions for completion of course
Evaluation based on: project (teaching period), oral exam
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_PZ_>
Notes
Enrollment limit: 20 students
<_SO_>
Brief outline of the course
Basic principles of designing experiments, block designs, factorial designs, response surface designs, optimal experimental designs for linear models (exact and approximate design, optimality criteria, algorithmic computing of optimal designs), optimal design of experiments for nonlinear models.
<_VH_>
Weighting of course assessment (continuous/final)
40/60
<_VV_>
Learning outcomes
The students will learn about basic principles of designing statistical experiments and the optimization of the quality of experiments.
A
38
62.3
B
11
18.03
C
6
9.84
D
2
3.28
E
3
4.92
FX
1
1.64
61
6
104880
B
2-PMS-109
FMFI.KAMŠ/2-PMS-109/15
Computer Statistics
3
Evaluation
lecture
2
26
2
(Single degree study, master II. deg., full time form)
L
1
Mgr. Ján Somorčík, PhD., doc. Mgr. Lenka Filová, PhD.
16.02.2026
11.03.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
A
4374
mEMMa
Mathematical Economics, Finance and Modelling
L
1
on-site learning
2
954
mPMS
Probability and Mathematical Statistics
L
1
on-site learning
2
4387
mPMSa
Probability and Mathematical Statistics
L
1
on-site learning
2
3965
mEMM
Mathematical Economics, Finance and Modelling
L
1
on-site learning
2
II.
on-site learning
true
II.
1049914
1
P
lecturer
Ján
Somorčík
Mgr. Ján Somorčík, PhD.
somorcik1@uniba.sk
Slovak
1014351
2
P
lecturer
Lenka
Filová
doc. Mgr. Lenka Filová, PhD.
filova2@uniba.sk
Slovak
SK
Slovak
Slovak
L
2
<_L_>
Recommended literature
Agresti A: Categorical Data Analysis 3rd ed. W Agresti A: Categorical Data Analysis 3rd ed. Wiley 2012;
Anděl J: Statistické metody. Matfyzpress 2007.
<_PA_>
Conditions for completion of course
project (teaching period), oral exam
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
1. Contingency tables, graphical representation, test of independence, homogeneity test, odds ratio, McNemar’s test, Simpson’s paradox and the Cochran-Mantel-Haenszel test, Bowker’s test, Fisher’s exact test.
2. Logistic regression: interpretation of parameters, probability vs. odds, deviance, tests of submodels, Wald tests and confidence intervals, graphical representation, pseudo coefficients of determination, logistic regression as a classifier.
3. Permutation versions of the t-test and ANOVA.
4. Bootstrap: estimation of variation, confidence intervals, application to regression, a demonstration of misuse.
<_URL_>
Course web page URL
http://www.iam.fmph.uniba.sk/ospm/Somorcik/teaching.htm
<_VH_>
Weighting of course assessment (continuous/final)
70/30
<_VV_>
Learning outcomes
Students will be able to perform some rather advanced statistical analyses, using the software R.
A
251
70.7
B
66
18.59
C
27
7.61
D
5
1.41
E
5
1.41
FX
1
0.28
355
6
81376
B
2-PMS-115
FMFI.KAMŠ/2-PMS-115/10
Multivariate Statistical Analysis
5
Evaluation
lecture / practicals
2 / 2
26 / 26
1
(Single degree study, master II. deg., full time form)
Z
1
doc. Mgr. Lenka Filová, PhD.
16.02.2026
24.06.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
practicals
2
26
prezenčná
A
965
mINF/k
Computer Science (Conversion Programme)
Z
1
on-site learning
1
4378
mINFa
Computer Science
Z
1
on-site learning
1
4387
mPMSa
Probability and Mathematical Statistics
Z
1
on-site learning
1
954
mPMS
Probability and Mathematical Statistics
Z
1
on-site learning
1
968
mINF
Computer Science
Z
1
on-site learning
1
II.
on-site learning
true
II.
1014351
1
P
lecturer
Lenka
Filová
doc. Mgr. Lenka Filová, PhD.
filova2@uniba.sk
Slovak
SK
Slovak
Slovak
Z
1
<_C_>
Goal
The students will have both theoretical and practical knowledge of the most used methods of multivariate statistical analyses.
<_L_>
Recommended literature
Härdle WK, Simar L: Applied multivariate statistical analysis, Springer, 2012; Härdle WK, Hlávka Z: Multivariate statistics: Exercises and solutions. Springer, 2007; Filová L, Szűcs G: Viacrozmerné štatistické analýzy, study materials of the lecturer, 2021.
<_P_>
Continuous assessment
test
<_PA_>
Conditions for completion of course
Evaluation based on: test (teaching period, 60%), oral exam (40%)
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_S_>
Course syllabus
1. Random vectors. (Random vectors, multivariate distributions, their density function, moments and characteristic function, transformations.)
2. Multinormal distribution. (Properties of multivariate normal distribution, distribution of subvectors, conditional normal distribution.)
3. Distribution of quadratic forms (Wishart distribution, Hotelling’s distribution.)
4. Estimating parameters in multivariate linear models. (The likelihood function, method of maximum likelihood, Cramer-Rao lower bound.)
5. Hypothesis testing in multivariate linear models. (Likelihood ratio test, testing hypotheses on the parameters of the normal distribution, linear hypothesis.)
6. Multivariate regression model. (Linear regression model, least squares method.)
7. Analysis of variance. (Analysis of variance model (ANOVA), models with repeated measurements, profile analysis, growth curve models, multivariate analysis of variance (MANOVA).)
8. Analysis of covariance.
<_SO_>
Brief outline of the course
Multivariate normal distribution and conditional distributions, Wishart distribution and Cochran theorem, Hotelling distribution, Wilks distribution, copulas, testing the hypotheses on parameters of the multivariate normal distribution, multivariate linear model, multivariate regression analysis, multivariate analysis of variance with one and two factors, covariance analysis, profile analysis, repeated measurements.
<_VH_>
Weighting of course assessment (continuous/final)
60/40
<_VV_>
Learning outcomes
The students will have both theoretical and practical knowledge of the most used methods of multivariate statistical analyses.
<_Z_>
Final assessment
exam
A
146
35.27
B
91
21.98
C
88
21.26
D
53
12.8
E
26
6.28
FX
10
2.42
414
6
124583
B
2-PMS-116
FMFI.KAMŠ/2-PMS-116/19
Cluster analysis and data classification
5
Evaluation
lecture / practicals
2 / 2
26 / 26
2
(Single degree study, master II. deg., full time form)
L
1
prof. Mgr. Radoslav Harman, PhD., Mgr. Samuel Rosa, PhD.
16.02.2026
10.03.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
practicals
2
26
prezenčná
A
4378
mINFa
Computer Science
L
1
on-site learning
2
4374
mEMMa
Mathematical Economics, Finance and Modelling
L
1
on-site learning
2
965
mINF/k
Computer Science (Conversion Programme)
L
1
on-site learning
2
954
mPMS
Probability and Mathematical Statistics
L
1
on-site learning
2
2979
mMAT
Mathematics
L
2
on-site learning
4
4383
mMATa
Mathematics
L
2
on-site learning
4
968
mINF
Computer Science
L
1
on-site learning
2
4387
mPMSa
Probability and Mathematical Statistics
L
1
on-site learning
2
3965
mEMM
Mathematical Economics, Finance and Modelling
L
1
on-site learning
2
II.
on-site learning
true
II.
1018338
1
P
lecturer
Radoslav
Harman
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
Slovak
1046621
2
P
lecturer
Samuel
Rosa
Mgr. Samuel Rosa, PhD.
rosa2@uniba.sk
Slovak
SK
Slovak
Slovak
L
2
<_L_>
Recommended literature
Izenman A: Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning (Springer Texts in Statistics) 1st ed., 2nd printing 2013; James G, Witten D, Hastie T, Tibshirani R: An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) 2nd ed., Springer 2021; Harman R: Multivariate Statistical Analysis (Selected Lecture Notes), study materials of the lecturer, 2021.
<_PA_>
Conditions for completion of course
Evaluation based on: project (teaching period), oral exam
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_PZ_>
Notes
The knowledge of the software R is recommended. Enrollment limit: 40 students
<_SO_>
Brief outline of the course
Selected multivariate data visualization methods, partitional clustering (k-means, k-medoids, DBSCAN, OPTICS, clustering based on the mixture of Gaussian distributions, spectral clustering), hierarchical clustering, general introduction to the statistical classification methods, Bayes classifier, k nearest neighbors, linear and quadratic discrimination, classification trees and forests, bagging and boosting, support vector machines, multinomial regression as a classification method
<_VH_>
Weighting of course assessment (continuous/final)
50/50
<_VV_>
Learning outcomes
The students understand the principles and practical realization of selected methods of cluster analysis and statistical data classification.
A
103
59.54
B
41
23.7
C
16
9.25
D
2
1.16
E
3
1.73
FX
8
4.62
173
6
133637
B
2-PMS-118
FMFI.KAMŠ/2-PMS-118/22
Markov Models (1)
5
Evaluation
lecture / practicals
2 / 2
26 / 26
1
(Single degree study, master II. deg., full time form)
Z
1
doc. RNDr. Katarína Janková, CSc., Mgr. Ján Veselý
16.02.2026
22.06.2022
FMFI.KAMŠ/2-PMS-118/10
FMFI.KAMŠ/2-PMS-118/10 - Markov Processes (1)
2-PMS-118 - Markov Processes (1)
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
practicals
2
26
prezenčná
86099
FMFI.KAMŠ/2-PMS-118/10
2-PMS-118
Markov Processes (1)
A
3965
mEMM
Mathematical Economics, Finance and Modelling
Z
1
on-site learning
1
4374
mEMMa
Mathematical Economics, Finance and Modelling
Z
1
on-site learning
1
2979
mMAT
Mathematics
Z
1
on-site learning
1
4383
mMATa
Mathematics
Z
1
on-site learning
1
4387
mPMSa
Probability and Mathematical Statistics
Z
1
on-site learning
1
954
mPMS
Probability and Mathematical Statistics
Z
1
on-site learning
1
II.
on-site learning
true
II.
1039018
1
P
lecturer
Katarína
Janková
doc. RNDr. Katarína Janková, CSc.
jankova1@uniba.sk
English, Slovak
1224799
2
C
instructor
Ján
Veselý
Mgr. Ján Veselý
vesely18@uniba.sk
English, Slovak
EN
English
SK
Slovak
English, Slovak
Z
1
<_C_>
Goal
To give an introduction to discrete time Markov chains
<_L_>
Recommended literature
Kalas, J: Markovove reťazce, skriptá MFF UK
Norris, J.R.: Markov chains (1998)
Ross, S.M.: Introduction to probability models (2006)
<_P_>
Continuous assessment
test
<_PA_>
Conditions for completion of course
Preliminary semester evaluation: test(20%) and homeworks(50%)
Examination: written examination(30%)
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_SO_>
Brief outline of the course
Markov property, transition probabilities, transition matrix, Chapman Kolmogorov equation, irreducibility of a chain. Classification of states, recurrent states, transient states, null recurrent states and positive recurrent states, periodicity. Existence of stationary distribution, ergodic distribution, necessary and sufficient conditions for ergodicity. Random walks, branching processes, absorbtion probabilities, mean time to absorbtion. Markov reward chains algorithms and Markov Chain Monte Carlo.
<_VV_>
Learning outcomes
After completing the course the student will master elementary discrete time Markov chains models. He will be able to classify states of a Markov chain and calculate stationary probability distributions.
<_Z_>
Final assessment
exam
A
73
24.33
B
68
22.67
C
75
25.0
D
58
19.33
E
23
7.67
FX
3
1.0
300
6
133645
B
2-PMS-119
FMFI.KAMŠ/2-PMS-119/22
Markov Models (2)
5
Evaluation
lecture / practicals
2 / 2
26 / 26
2
(Single degree study, master II. deg., full time form)
L
1
doc. RNDr. Katarína Janková, CSc.
16.02.2026
22.06.2022
FMFI.KAMŠ/2-PMS-119/15
FMFI.KAMŠ/2-PMS-119/15 - Markov Processes(2)
2-PMS-119 - Markov Processes(2)
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
practicals
2
26
prezenčná
104882
FMFI.KAMŠ/2-PMS-119/15
2-PMS-119
Markov Processes(2)
A
4383
mMATa
Mathematics
L
1
on-site learning
2
954
mPMS
Probability and Mathematical Statistics
L
1
on-site learning
2
2979
mMAT
Mathematics
L
1
on-site learning
2
4387
mPMSa
Probability and Mathematical Statistics
L
1
on-site learning
2
II.
on-site learning
true
II.
1039018
1
P
lecturer
Katarína
Janková
doc. RNDr. Katarína Janková, CSc.
jankova1@uniba.sk
Slovak
SK
Slovak
Slovak
L
2
<_L_>
Recommended literature
Janková, K., Kilianová, S., Brunovský, P., Bokes, P.: Markovove reťazce a ich aplikácie. Epos 2014.
Norris, J.:Markov Chains.Cambridge University Press 1997.
<_PA_>
Conditions for completion of course
20% test(teaching period), 30% homeworks(teaching period), 50% examination
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_SO_>
Brief outline of the course
Markov property for continuous time chains, probabilities of transition, initial distribution, Chapman Kolmogorov equation. Forces of transition and their properties, backward and forward systems of Kolmogorov differential equations. Statioanary and ergodic distribution of the chain. Models of linear growth, birth and death chains, Poisson process. Characterization of processes using jump chain and holding times. Queueing systems: M/M/n, M/M/infinity. Imbedded chain technique for M/G/1. Pollaczek Chinchin formula.
<_VV_>
Learning outcomes
After completing the course students will know properties of homogeneous Markov chains with continuous time. They will be able to use models based on these chains.
A
36
28.13
B
20
15.63
C
33
25.78
D
30
23.44
E
7
5.47
FX
2
1.56
128
6
133639
B
2-PMS-121
FMFI.KAMŠ/2-PMS-121/22
Sequential Statistical Methods
3
Evaluation
lecture
2
26
3
(Single degree study, master II. deg., full time form)
Z
2
doc. RNDr. Katarína Janková, CSc.
16.02.2026
22.06.2022
FMFI.KAMŠ/2-PMS-121/00
FMFI.KAMŠ/2-PMS-121/00 - Sequential Methods
2-PMS-121 - Sequential Methods
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
30024
FMFI.KAMŠ/2-PMS-121/00
2-PMS-121
Sequential Methods
A
954
mPMS
Probability and Mathematical Statistics
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
2
on-site learning
3
II.
on-site learning
true
II.
1039018
1
P
lecturer
Katarína
Janková
doc. RNDr. Katarína Janková, CSc.
jankova1@uniba.sk
Slovak
SK
Slovak
Slovak
Z
3
<_C_>
Goal
To acquaint students with basic sequential methods of parameter estimation and testing statistical hypothesis.
<_L_>
Recommended literature
Hušková: Sekvenční analýza.
Rao: Lineární metody statistické indukce a jejich aplikace.
Govindarajulu, Z: Sequential statistics (2004)
<_P_>
Continuous assessment
test, homeworks
<_PA_>
Conditions for completion of course
Preliminary semester evaluation: test(30%) and homeworks(30%)
Final examination: written examination(40%)
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_SO_>
Brief outline of the course
Sequential hypothesis testing, sequential tests for quality control. Wald sequential test, its properties and characteristics. Wald's identities and their applications for operation characteristics and average sample number of a sequential test. Testing composite hypotheses, Sobel Wald test, comparison of parameters of two distributions. Sequential point estimation, Rao-Cramer theorem, Stein's two stage procedure. Sequential confidence intervals.
<_VV_>
Learning outcomes
Upon satisfactory completion of the course the student will be able to use sequential procedures for the statistical hypothesis testing and the estimation of unknown parameters.
<_Z_>
Final assessment
examination
A
77
34.68
B
57
25.68
C
49
22.07
D
28
12.61
E
10
4.5
FX
1
0.45
222
6
81386
B
2-PMS-123
FMFI.KAMŠ/2-PMS-123/10
Stochastic Simulation Methods
3
Evaluation
lecture
2
26
3
(Single degree study, master II. deg., full time form)
Z
2
doc. Mgr. Pavol Bokes, PhD.
16.02.2026
10.03.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
A
2979
mMAT
Mathematics
Z
2
on-site learning
3
3965
mEMM
Mathematical Economics, Finance and Modelling
Z
1
on-site learning
1
954
mPMS
Probability and Mathematical Statistics
Z
2
on-site learning
3
4374
mEMMa
Mathematical Economics, Finance and Modelling
Z
1
on-site learning
1
965
mINF/k
Computer Science (Conversion Programme)
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
2
on-site learning
3
4378
mINFa
Computer Science
Z
2
on-site learning
3
4383
mMATa
Mathematics
Z
2
on-site learning
3
968
mINF
Computer Science
Z
2
on-site learning
3
II.
on-site learning
true
II.
1079006
1
P
lecturer
Pavol
Bokes
doc. Mgr. Pavol Bokes, PhD.
bokes3@uniba.sk
Slovak
SK
Slovak
Slovak
Z
3
<_L_>
Recommended literature
Ross S: Simulation, Elsevier Academic Press 2006; Study materials of the lecturer.
<_PA_>
Conditions for completion of course
Evaluation based on: project (teaching period), oral exam
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_PZ_>
Notes
Enrollment limit: 40 students
<_SO_>
Brief outline of the course
Generating random numbers, testing of random number generators, Sampling discrete random variables and vectors, Sampling continuous random variables and vectors, Statistical analysis of simulated data, Classical Monte Carlo methods.
<_VH_>
Weighting of course assessment (continuous/final)
50/50
<_VV_>
Learning outcomes
The students understand the basic methods of computer sampling of random variables and vectors, apply them to the computation of Monte Carlo estimates and use them for the simulation of complex stochastic systems.
A
222
44.85
B
119
24.04
C
65
13.13
D
42
8.48
E
29
5.86
FX
18
3.64
495
6
133636
B
2-PMS-129
FMFI.KAMŠ/2-PMS-129/22
Stochastic Optimization Methods
2
Evaluation
lecture
2
26
1, 3
(Single degree study, master II. deg., full time form)
Z
1, 2
prof. Mgr. Radoslav Harman, PhD.
16.02.2026
10.03.2022
FMFI.KAMŠ/2-PMS-129/10
FMFI.KAMŠ/2-PMS-129/10 - Stochastic Optimization Methods
2-PMS-129 - Stochastic Optimization Methods
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
81384
FMFI.KAMŠ/2-PMS-129/10
2-PMS-129
Stochastic Optimization Methods
C
964
mMMN
Managerial Mathematics
Z
1
on-site learning
1
4374
mEMMa
Mathematical Economics, Finance and Modelling
Z
2
on-site learning
3
954
mPMS
Probability and Mathematical Statistics
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
2
on-site learning
3
964
mMMN
Managerial Mathematics
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
1
on-site learning
1
4382
mMMNa
Managerial Mathematics
Z
1
on-site learning
1
4382
mMMNa
Managerial Mathematics
Z
2
on-site learning
3
954
mPMS
Probability and Mathematical Statistics
Z
1
on-site learning
1
4889
DAV
Data Science
Z
3
on-site learning
5
4891
DAV/k
Data Science (Conversion Programme)
Z
4
on-site learning
7
3965
mEMM
Mathematical Economics, Finance and Modelling
Z
2
on-site learning
3
I., II.
on-site learning
true
I., II.
1018338
1
P
lecturer
Radoslav
Harman
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
Slovak
SK
Slovak
Slovak
Z
1
3
<_L_>
Recommended literature
Luke S: Essentials of Metaheuristics, Lulu, 2013; Study materials of the lecturer.
<_PA_>
Conditions for completion of course
Evaluation based on: project (teaching period)
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
A brief general overview of optimization. Various types of optimization problems and optimization algorithms. Construction of the initial solution. The basic heuristic optimization algorithms. Covariance matrix adaptation. Simulated annealing. Algorithm Nelder-Mead. Genetic algorithms. Differential evolution. Particle swarm optimization. Basic principles of constrained optimization.
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
The students understand the algorithmic principles of a variety of optimization methods, mostly heuristics with stochastic elements, applicable to the problems of discrete optimization as well as non-convex continuous optimization.
A
165
69.62
B
40
16.88
C
16
6.75
D
8
3.38
E
3
1.27
FX
5
2.11
237
6
81383
B
2-PMS-130
FMFI.KAMŠ/2-PMS-130/10
Categorial Data Analysis
3
Evaluation
lecture
2
26
3
(Single degree study, master II. deg., full time form)
Z
2
Mgr. Ján Somorčík, PhD., doc. Mgr. Lenka Filová, PhD.
16.02.2026
11.03.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
A
954
mPMS
Probability and Mathematical Statistics
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
2
on-site learning
3
II.
on-site learning
true
II.
1049914
1
P
lecturer
Ján
Somorčík
Mgr. Ján Somorčík, PhD.
somorcik1@uniba.sk
Slovak
1014351
2
P
lecturer
Lenka
Filová
doc. Mgr. Lenka Filová, PhD.
filova2@uniba.sk
Slovak
SK
Slovak
Slovak
Z
3
<_L_>
Recommended literature
Agresti A: Categorical Data Analysis 3rd ed. Wiley 2012.
<_PA_>
Conditions for completion of course
project (teaching period), written exam
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
1. Maximum likelihood method in the world of cathegorical data: the Holy Trinity (Wald test, score test, likelihood ratio test). Corresponding confidence intervals for the binomial parameter (Wald’s, Wilson’s, Agresti-Coull’s, based on the likelihood ratio test).
2. Inference about a vector parameter: the Holy Trinity of tests, the Pearson’s chi-squared test and the connection between them, a multinomial example.
3. Contingency table: connection with the multinomial distribution, testing of general hypotheses about the parameters, independence testing.
4.
Delta-method: the principle, demonstrations contingency tables in case of inference about odds and odd ratio.
5. Measures of correlation of non-numeric variables (Goodman-Kruskal gamma).
6. Equivalence between the multinomial models of contingency tables and the corresponding Poisson log-linear model.
7. Poisson log-linear model: interpretation of parameters, the saturated model, deviance.
8. Saturated 3-way Poisson log-linear model: interpretation of parameters, visualization using a mosaic plot, interpretation and testing of submodels, confidence intervals for parameters.
9. A test about the odds ratio, the Cochran-Mantel-Haenszel test and the Woolf’s test in 3-dimensional 2x2xK contingency tables as tests about submodels of a Poisson log-linear model.
10. Logistic and multinomial regression as special cases of a Poisson log-linear model.
<_URL_>
Course web page URL
http://www.iam.fmph.uniba.sk/ospm/Somorcik/teaching.htm
<_VH_>
Weighting of course assessment (continuous/final)
50/50
<_VV_>
Learning outcomes
The students understand the principles of modeling of non-numeric data and the maximum likelihood inference in such models. They also realize that many seemingly unrelated classical methods of categorical data analysis are just special cases of a simple and unified approach by means of generalized linear models.
A
111
61.33
B
37
20.44
C
17
9.39
D
7
3.87
E
9
4.97
FX
0
0.0
181
6
133676
B
2-PMS-131
FMFI.KAMŠ/2-PMS-131/22
Survival Analysis
2
Evaluation
lecture
2
26
1, 3
(Single degree study, master II. deg., full time form)
Z
1, 2
doc. Mgr. Lenka Filová, PhD.
16.02.2026
16.06.2022
FMFI.KAMŠ/2-PMS-131/17
FMFI.KAMŠ/2-PMS-131/17 - Survival Analysis
2-PMS-131 - Survival Analysis
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
118813
FMFI.KAMŠ/2-PMS-131/17
2-PMS-131
Survival Analysis
C
954
mPMS
Probability and Mathematical Statistics
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
1
on-site learning
1
954
mPMS
Probability and Mathematical Statistics
Z
1
on-site learning
1
II.
on-site learning
true
II.
1014351
1
P
lecturer
Lenka
Filová
doc. Mgr. Lenka Filová, PhD.
filova2@uniba.sk
Slovak
SK
Slovak
Slovak
Z
1
3
<_L_>
Recommended literature
Collett, David. Modelling survival data in medical research. CRC press, 2015.
Kleinbaum, David G., and Mitchel Klein. Survival analysis. Springer,, 2010.
<_PA_>
Conditions for completion of course
Evaluation based on: project (teaching period)
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
Censoring, survival function and hazard function. Discrete time models. Nonparametric estimates. Kaplan-Meier estimate. Comparing two survival datasets. Log-rank test. Wilcoxon test. Proportional hazards model. Likelihood function for proportional hazards model parameters. Weibull model as a proportional hazards model. Log-linear form of the Weibull distribution. More distributions used in survival analysis. Diagnostics in proportional hazards model. Cox-Snell residuals. Martingale residuals. Aft (accelerated failure time) models. Cox regression model. Models with time-dependent explanatory variables.
<_URL_>
Course web page URL
http://www.iam.fmph.uniba.sk/ospm/Filova/teaching.htm
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
The students will gain knowledge about selected statistical methods used in analysis and modelling of censored data, mainly in medical applications.
A
26
89.66
B
0
0.0
C
2
6.9
D
0
0.0
E
0
0.0
FX
1
3.45
29
6
30215
B
2-PMS-135
FMFI.KAMŠ/2-PMS-135/00
Pensions and Pension Funds
3
Evaluation
lecture
2
26
2, 4
(Single degree study, master II. deg., full time form)
L
1, 2
doc. Mgr. Igor Melicherčík, PhD., Mgr. Gábor Szűcs, PhD.
16.02.2026
12.12.2025
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
C
964
mMMN
Managerial Mathematics
L
2
on-site learning
4
954
mPMS
Probability and Mathematical Statistics
L
2
on-site learning
4
4374
mEMMa
Mathematical Economics, Finance and Modelling
L
1
on-site learning
2
964
mMMN
Managerial Mathematics
L
1
on-site learning
2
4382
mMMNa
Managerial Mathematics
L
1
on-site learning
2
4382
mMMNa
Managerial Mathematics
L
2
on-site learning
4
954
mPMS
Probability and Mathematical Statistics
L
1
on-site learning
2
4387
mPMSa
Probability and Mathematical Statistics
L
2
on-site learning
4
4387
mPMSa
Probability and Mathematical Statistics
L
1
on-site learning
2
3965
mEMM
Mathematical Economics, Finance and Modelling
L
1
on-site learning
2
II.
on-site learning
true
II.
1009661
1
P
lecturer
Igor
Melicherčík
doc. Mgr. Igor Melicherčík, PhD.
melichercik2@uniba.sk
Slovak
1022694
2
P
lecturer
Gábor
Szűcs
Mgr. Gábor Szűcs, PhD.
szucs1@uniba.sk
Slovak
SK
Slovak
Slovak
L
2
4
<_L_>
Recommended literature
Penze: kvantitativní přístup / Tomáš Cipra, Praha : Ekopress, 2012;
Pensions at a Glance 2019 / OECD and G20 Indicators, Paris : OECD Publishing, 2019; https://doi.org/10.1787/b6d3dcfc-en;
An Introduction to Pension Schemes / E. M. Lee, London : Institute and Faculty of Actuaries, 1986;
study materials of lecturers.
<_PA_>
Conditions for completion of course
During the semester, the student can get 50% of the assessment for one presentation. The final oral exam has a weight of 50%.
Grade thresholds: A: at least 90.00%; B: 89.99% – 80.00%; C: 79.99% – 70.00%; D: 69.99% – 60.00%; E: 59.99% – 50.00%; Fx: 49.99% – 0.00%.
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
Three pillars of pension system: compulsory, supplementary, personal. Pension system in Slovakia. The pay-as-you-go system, funded pension scheme. Defined benefit (DB) plans and defined contribution (DC) plans. Benefits not depending on previous earnings, depending on average salary or last salaries. One-time lump sum death benefit. Transfers. Funding plans. Pension funds investment.
<_VH_>
Weighting of course assessment (continuous/final)
Practical work during semester 50% / final exam 50%.
<_VV_>
Learning outcomes
After completing the course, the student will know the basic principles and functions of pension schemes. The student will get acquainted with the pension system of the Slovak Republic and old-age pension schemes of some other countries of the world. The student will be able to valuate assets and liabilities of various types of pension systems, such as defined benefit (DB) schemes and defined contribution (DC) schemes.
A
55
65.48
B
16
19.05
C
7
8.33
D
0
0.0
E
5
5.95
FX
1
1.19
84
6
133866
B
2-PMS-136
FMFI.KAMŠ/2-PMS-136/22
Biostatistics
3
Evaluation
lecture
2
26
2
(Single degree study, master II. deg., full time form)
L
1
doc. Mgr. Lenka Filová, PhD.
16.02.2026
16.06.2022
FMFI.KAMŠ/2-PMS-136/20
FMFI.KAMŠ/2-PMS-136/20 - Biostatistics
2-PMS-136 - Biostatistics
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
127672
FMFI.KAMŠ/2-PMS-136/20
2-PMS-136
Biostatistics
A
954
mPMS
Probability and Mathematical Statistics
L
1
on-site learning
2
4387
mPMSa
Probability and Mathematical Statistics
L
1
on-site learning
2
II.
on-site learning
true
II.
1014351
1
P
lecturer
Lenka
Filová
doc. Mgr. Lenka Filová, PhD.
filova2@uniba.sk
Slovak
SK
Slovak
Slovak
L
2
<_L_>
Recommended literature
Gałecki A, Burzykowski T.: Linear mixed-effects model Using R. Springer, New York, NY (2013); Filová L: Biostatistics, study materials of the lecturer, 2021.
<_O_>
Content prerequisite
Multivariate statistical analyses 2-PMS-115; Regression models 2-PMS-107
<_PA_>
Conditions for completion of course
Evaluation based on: project (teaching period)
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
Fixed and random effects models; Analysis of variance with random effects; Linear models with random effects; Metaanalysis and metaregression; Repeated and longitudinal measurements; Diagnostic tests, sensitivity, specificity and combined measures of accuracy; ROC curves and their characteristics.
<_URL_>
Course web page URL
http://www.iam.fmph.uniba.sk/ospm/Filova/teaching.htm
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
The students will gain knowledge about selected statistical methods used in biomedical applications.
A
26
56.52
B
12
26.09
C
8
17.39
D
0
0.0
E
0
0.0
FX
0
0.0
46
6
104883
B
2-PMS-141
FMFI.KAMŠ/2-PMS-141/15
Probability Theory
5
Evaluation
lecture / practicals
2 / 2
26 / 26
1
(Single degree study, master II. deg., full time form)
Z
1
Mgr. Ján Somorčík, PhD., doc. RNDr. Katarína Janková, CSc.
16.02.2026
11.03.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
practicals
2
26
prezenčná
A
4387
mPMSa
Probability and Mathematical Statistics
Z
1
on-site learning
1
954
mPMS
Probability and Mathematical Statistics
Z
1
on-site learning
1
II.
on-site learning
true
II.
1049914
1
P
lecturer
Ján
Somorčík
Mgr. Ján Somorčík, PhD.
somorcik1@uniba.sk
Slovak
1039018
2
P
lecturer
Katarína
Janková
doc. RNDr. Katarína Janková, CSc.
jankova1@uniba.sk
Slovak
SK
Slovak
Slovak
Z
1
<_L_>
Recommended literature
Williams D: Probability with Martingales. Cambridge University Press 1991;
Melicherčík I: Kapitoly z finančnej matematiky. Epos 2006;
Bhattacharya R N, Waymire E C: Stochastic Processes with Applications. SIAM 2009;
Bühlmann H: Life Insurance with Stochastic Interest Rates. In: Ottaviani G (eds) Financial Risk in Insurance. Springer 2000.
<_PA_>
Conditions for completion of course
test (teaching period), written exam
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
1. How to understands the concepts of random event and random variable. Conditions of measurability of a mapping; applications of the Borel-Cantelli lema.
2. Basics of the signed measures theory: positive and negative sets, set of zero signed measure in the strong sense, Hahn decomposition, Jordan decomposition, absolute continuity, Radon-Nikodym theorem, application of the Radon-Nikodym derivative via the change of variable theorem.
3. Sigma-algebra as a representative of extra information, filtration; conditional expectation given by a sigma-algebra (geometric and probabilistic approach).
4. Martingale, supermartingale, submartingale; sigma-algebra generated by random variables; stopping time.
5. Some winning betting strategies in gambling (e.g. the martingale betting strategy); Doob's optional stopping theorem and its applications to gambling of one or more players, to random walks, and to evaluation of some expected stopping times in gambling.
6. Tower property of conditional expectations and a corollary; properties of martingale increments; ways to produce a martingale.
7. Applications of conditional expectations and martingales to insurance: model with random interests, valuation of the policy, technical financial and total loss of the insurer; extensions of the Hattendorff’s theorem to the case of random interests and financial or technical losses.
<_URL_>
Course web page URL
http://www.iam.fmph.uniba.sk/ospm/Somorcik/teaching.htm
<_VH_>
Weighting of course assessment (continuous/final)
40/60
<_VV_>
Learning outcomes
The student understands various advanced probabilistic concepts and statements based on the theory of measures and integration. He also comprehends the basic properties of martingales and knows about some of their applications.
A
46
35.11
B
18
13.74
C
24
18.32
D
25
19.08
E
17
12.98
FX
1
0.76
131
6
133635
B
2-PMS-142
FMFI.KAMŠ/2-PMS-142/22
Data Dimensionality Reduction
4
Evaluation
lecture / practicals
2 / 1
26 / 13
2
(Single degree study, master II. deg., full time form)
L
1
Mgr. Samuel Rosa, PhD.
16.02.2026
18.06.2022
FMFI.KAMŠ/2-PMS-142/19
FMFI.KAMŠ/2-PMS-142/19 - Data Dimensionality Reduction
2-PMS-142 - Data Dimensionality Reduction
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
practicals
1
13
prezenčná
124593
FMFI.KAMŠ/2-PMS-142/19
2-PMS-142
Data Dimensionality Reduction
A
4378
mINFa
Computer Science
L
1
on-site learning
2
4374
mEMMa
Mathematical Economics, Finance and Modelling
L
1
on-site learning
2
965
mINF/k
Computer Science (Conversion Programme)
L
1
on-site learning
2
954
mPMS
Probability and Mathematical Statistics
L
1
on-site learning
2
968
mINF
Computer Science
L
1
on-site learning
2
4387
mPMSa
Probability and Mathematical Statistics
L
1
on-site learning
2
3965
mEMM
Mathematical Economics, Finance and Modelling
L
1
on-site learning
2
II.
on-site learning
true
II.
1046621
1
P
lecturer
Samuel
Rosa
Mgr. Samuel Rosa, PhD.
rosa2@uniba.sk
Slovak
SK
Slovak
Slovak
L
2
<_L_>
Recommended literature
Izenman A: Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning (Springer Texts in Statistics) 1st ed., 2nd printing 2013;
James G, Witten D, Hastie T, Tibshirani R: An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) 2nd ed., Springer, 2021;
Hastie T, Tibshirani R, Friedman J: The Elements of Statistical Learning, 2nd ed., Springer, 2016
<_PA_>
Conditions for completion of course
Evaluation based on: project (teaching period), oral exam
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_PZ_>
Notes
An electronic version of "An Introduction to Statistical Learning" by James et al. is available at: https://www.statlearning.com/
<_SO_>
Brief outline of the course
1. Linear methods of feature extraction – principal component analysis, factor analysis, projection pursuit.
2. Nonlinear methods of feature extraction – nonlinear principal component analysis, metric and nonmetric multidimensional scaling, isomap, t-SNE.
3. Feature selection methods – forward and backward selection, lasso, ridge regression.
4. Subsampling methods – random subsample, leveraging methods.
<_URL_>
Course web page URL
http://www.iam.fmph.uniba.sk/ospm/Rosa/teaching.htm
<_VH_>
Weighting of course assessment (continuous/final)
50/50
<_VV_>
Learning outcomes
The students will familiarize themselves with the methods of feature extraction and selection and with subsample selection. They will also acquire the ability to apply these methods in practical data analysis.
A
69
45.7
B
42
27.81
C
22
14.57
D
7
4.64
E
5
3.31
FX
6
3.97
151
6
104884
B
2-PMS-212
FMFI.KAMŠ/2-PMS-212/15
Nonparametric Statistics
3
Evaluation
lecture
2
26
4
(Single degree study, master II. deg., full time form)
L
2
Mgr. Ján Somorčík, PhD., doc. Mgr. Lenka Filová, PhD.
16.02.2026
11.03.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
A
954
mPMS
Probability and Mathematical Statistics
L
2
on-site learning
4
3965
mEMM
Mathematical Economics, Finance and Modelling
L
2
on-site learning
4
4387
mPMSa
Probability and Mathematical Statistics
L
2
on-site learning
4
4374
mEMMa
Mathematical Economics, Finance and Modelling
L
2
on-site learning
4
II.
on-site learning
true
II.
1049914
1
P
lecturer
Ján
Somorčík
Mgr. Ján Somorčík, PhD.
somorcik1@uniba.sk
Slovak
1014351
2
P
lecturer
Lenka
Filová
doc. Mgr. Lenka Filová, PhD.
filova2@uniba.sk
Slovak
SK
Slovak
Slovak
L
4
<_L_>
Recommended literature
Rublík F: Neparametrické metódy. Veda 2011;
Lehmann E: Nonparametrics: Statistical Methods Based on Ranks (revised edition). Springer 2006;
Hollander M, Wolfe D A, Chicken E: Nonparametric statistical methods 3rd ed. Wiley 2013;
<_PA_>
Conditions for completion of course
written exam
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
Sign test and a confidence interval for the true median. Wilcoxon signed rank test, Hodges-Lehmann estimator of location and the corresponding confidence interval. Sign test and Wilcoxon test for paired data. Wilcoxon rank sum test and Mann-Whitney test. Hodges-Lehmann estimator of shift and the corresponding confidence interval. Problem of ties. Kruskal-Wallis test and some post-hoc tests. Spearmans’s rho, Kendall’s tau. Theil’s tests, estimators and confidence intervals concerning parameters of simple linear regression. Estimator's robustness to outliers (breakdown point). Kolmogorov-Smirnov tests. Cramér-von Mises test. Multimariate tests: component-wise sign test, Rayleigh test, Randles test by means of interdirections. Availability of the discussed methods in the software R.
<_URL_>
Course web page URL
http://www.iam.fmph.uniba.sk/ospm/Somorcik/teaching.htm
<_VH_>
Weighting of course assessment (continuous/final)
0/100
<_VV_>
Learning outcomes
Students are able to apply basic nonparametric methods to real data. They also understand the principles and the mathematical background of these methods.
A
142
68.6
B
24
11.59
C
16
7.73
D
12
5.8
E
9
4.35
FX
4
1.93
207
6
133641
B
2-PMS-213
FMFI.KAMŠ/2-PMS-213/22
Bayesian Methods (1)
3
Evaluation
lecture
2
26
3
(Single degree study, master II. deg., full time form)
Z
2
Mgr. Jozef Kováč, PhD., prof. Mgr. Radoslav Harman, PhD.
16.02.2026
10.03.2022
FMFI.KAMŠ/2-PMS-213/00
FMFI.KAMŠ/2-PMS-213/00 - Bayesian Statistics
2-PMS-213 - Bayesian Statistics
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
28278
FMFI.KAMŠ/2-PMS-213/00
2-PMS-213
Bayesian Statistics
A
954
mPMS
Probability and Mathematical Statistics
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
2
on-site learning
3
II.
on-site learning
true
II.
1058242
1
P
lecturer
Jozef
Kováč
Mgr. Jozef Kováč, PhD.
kovac59@uniba.sk
Slovak
1018338
2
P
lecturer
Radoslav
Harman
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
Slovak
SK
Slovak
Slovak
Z
3
<_L_>
Recommended literature
Hoff P.D.: A first course in Bayesian statistical methods. New York: Springer 2009.;
Pázman A.: Bayesovská štatistika, Bratislava: Univerzita Komenského 2009;
<_P_>
Continuous assessment
exam
<_PA_>
Conditions for completion of course
Preliminary semester evaluation: exam;
Examination: test and oral examination;
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_PZ_>
Notes
Enrollment limit: 25 students
<_SO_>
Brief outline of the course
Bayes’ theorem, prior and posterior predictive distributions, statistical decision theory and its application in Bayesian estimation and Bayesian testing, credible intervals, conjugate prior distributions, noninformative priors, information, entropy, I-divergence.
<_VH_>
Weighting of course assessment (continuous/final)
30/70
<_VV_>
Learning outcomes
Students will be introduced to the basic notions, concepts and principles of Bayesian statistics.
<_Z_>
Final assessment
examination
A
52
22.81
B
37
16.23
C
58
25.44
D
42
18.42
E
35
15.35
FX
4
1.75
228
6
133647
B
2-PMS-214
FMFI.KAMŠ/2-PMS-214/22
Bayesian Methods (2)
2
Evaluation
lecture
2
26
4
(Single degree study, master II. deg., full time form)
L
2
Mgr. Jozef Kováč, PhD., prof. Mgr. Radoslav Harman, PhD.
16.02.2026
03.05.2023
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
A
954
mPMS
Probability and Mathematical Statistics
L
2
on-site learning
4
4387
mPMSa
Probability and Mathematical Statistics
L
2
on-site learning
4
II.
on-site learning
true
II.
1058242
1
P
lecturer
Jozef
Kováč
Mgr. Jozef Kováč, PhD.
kovac59@uniba.sk
Slovak
1018338
2
P
lecturer
Radoslav
Harman
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
Slovak
SK
Slovak
Slovak
L
4
<_L_>
Recommended literature
Lambert B.: A Student’s Guide to Bayesian Statistics. California: Sage 2018;
Hoff P.D.: A first course in Bayesian statistical methods. New York: Springer 2009;
Neapolitan R.E.: Learning Bayesian networks. New Jersey: Pearson Prentice Hall 2004.
<_PA_>
Conditions for completion of course
homework
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_PZ_>
Notes
Enrollment limit: 25 students
<_SO_>
Brief outline of the course
MCMC in Bayesian methods, Metropolis-Hastings algorithm, Gibbs algorithm, Bayesian regression, hierarchical models, model validation, model averaging, Bayesian networks, Bayesian methods in machine learning.
<_VV_>
Learning outcomes
Students will gain an overview of practical applications of Bayesian methods and will be able to use them in R software.
A
16
53.33
B
7
23.33
C
3
10.0
D
3
10.0
E
1
3.33
FX
0
0.0
30
6
133677
B
2-PMS-221
FMFI.KAMŠ/2-PMS-221/22
Statistical methods in clinical trials
2
Evaluation
lecture
2
26
1, 3
(Single degree study, master II. deg., full time form)
Z
1, 2
doc. Mgr. Lenka Filová, PhD.
16.02.2026
16.06.2022
FMFI.KAMŠ/2-PMS-221/14
FMFI.KAMŠ/2-PMS-221/14 - Statistical methods in clinical trials
2-PMS-221 - Statistical methods in clinical trials
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
lecture
2
26
prezenčná
102443
FMFI.KAMŠ/2-PMS-221/14
2-PMS-221
Statistical methods in clinical trials
C
3965
mEMM
Mathematical Economics, Finance and Modelling
Z
1
on-site learning
1
4374
mEMMa
Mathematical Economics, Finance and Modelling
Z
2
on-site learning
3
954
mPMS
Probability and Mathematical Statistics
Z
2
on-site learning
3
4374
mEMMa
Mathematical Economics, Finance and Modelling
Z
1
on-site learning
1
4387
mPMSa
Probability and Mathematical Statistics
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
1
on-site learning
1
954
mPMS
Probability and Mathematical Statistics
Z
1
on-site learning
1
3965
mEMM
Mathematical Economics, Finance and Modelling
Z
2
on-site learning
3
II.
on-site learning
true
II.
1014351
1
P
lecturer
Lenka
Filová
doc. Mgr. Lenka Filová, PhD.
filova2@uniba.sk
Slovak
SK
Slovak
Slovak
Z
1
3
<_L_>
Recommended literature
O'Quigley J., Iasonos A., Bornkamp B. (Eds.): Handbook of methods for designing, monitoring, and analyzing dose-finding trials. CRC Press (2017); Filová L: Statistical methods in clinical trials, lecture notes (2021)
<_PA_>
Conditions for completion of course
Evaluation based on: project (teaching period)
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_PZ_>
Notes
Enrollment limit: 20 students
<_SO_>
Brief outline of the course
Phases and aims of clinical trials, blinding, randomization, maximum tolerated dose, minimum effective dose. Designs in phase 1 of clinical trials: 3+3 design, group design for determining toxicity, continual reassessment method. Designs for phase 2 of clinical trials: two-stage designs, dose-response modeling, estimating target doses, multiple comparisons method. Designs for phase 3 of clinical trials: group sequential methods, adaptive designs. Determining the optimal sample size. Late onset toxicity trials.
<_URL_>
Course web page URL
http://www.iam.fmph.uniba.sk/ospm/Filova/teaching.htm
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
The students will gain knowledge about statistical methods used in the design of clinical trials.
A
34
100.0
B
0
0.0
C
0
0.0
D
0
0.0
E
0
0.0
FX
0
0.0
34
6
133675
B
2-PMS-222
FMFI.KAMŠ/2-PMS-222/22
Programming in SAS
2
Evaluation
course
2
26
1, 3
(Single degree study, master II. deg., full time form)
Z
1, 2
Mgr. Jozef Kováč, PhD.
16.02.2026
16.06.2022
FMFI.KAMŠ/2-PMS-222/15
FMFI.KAMŠ/2-PMS-222/15 - Programming in SAS
2-PMS-222 - Programming in SAS
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
course
2
26
prezenčná
107477
FMFI.KAMŠ/2-PMS-222/15
2-PMS-222
Programming in SAS
C
964
mMMN
Managerial Mathematics
Z
1
on-site learning
1
954
mPMS
Probability and Mathematical Statistics
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
2
on-site learning
3
964
mMMN
Managerial Mathematics
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
1
on-site learning
1
4382
mMMNa
Managerial Mathematics
Z
1
on-site learning
1
4382
mMMNa
Managerial Mathematics
Z
2
on-site learning
3
954
mPMS
Probability and Mathematical Statistics
Z
1
on-site learning
1
II.
on-site learning
true
II.
1058242
1
P
lecturer
Jozef
Kováč
Mgr. Jozef Kováč, PhD.
kovac59@uniba.sk
Slovak
SK
Slovak
Slovak
Z
1
3
<_L_>
Recommended literature
SAS Institute Inc. 2001: Step-by-Step Programming with Base SAS® Software, Cary, NC: SAS Institute Inc.
<_PA_>
Conditions for completion of course
Preliminary semester evaluation: independent practice (80%), final exam (20%);
Approximate grade thresholds: A 91%, B 81%, C 71%, D 61%, E 51%
<_PJ_>
Language, which knowledge is needed to pass the course
slovak, english
<_PZ_>
Notes
Enrollment limit: 25 students
<_SO_>
Brief outline of the course
Basics of SAS language, creating SAS data from various sources (e.g. text files), data processing and reporting, data concatenation, data merging, numeric and text functions, identification of syntax and logic errors, basic SAS procedures.
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
Students will learn the basics of SAS programming language and will be able to access and process databases of various formats.
A
42
56.0
B
25
33.33
C
5
6.67
D
0
0.0
E
3
4.0
FX
0
0.0
75
6
133674
B
2-PMS-223
FMFI.KAMŠ/2-PMS-223/22
Probabilistic and Statistical Methods of Artificial Intelligence
2
Evaluation
seminar
2
26
3
(Single degree study, master II. deg., full time form)
Z
2
prof. Mgr. Radoslav Harman, PhD.
16.02.2026
13.03.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
seminar
2
26
prezenčná
C
954
mPMS
Probability and Mathematical Statistics
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
2
on-site learning
3
II.
on-site learning
true
II.
1018338
1
P
lecturer
Radoslav
Harman
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
Slovak
SK
Slovak
Slovak
Z
3
<_L_>
Recommended literature
Hastie T, Tibshirani R, Friedman J: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) 2nd Edition, Springer 2016; Russell S, Norvig P: Artificial Intelligence: A Modern Approach (Pearson Series in Artificial Intelligence) 4th Edition, Pearson, 2020
<_PA_>
Conditions for completion of course
Evaluation based on: active participation (teaching period)
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_PZ_>
Notes
Enrollment limit: 20 students
<_SO_>
Brief outline of the course
The topic of the seminar will be selected at the beginning of the semester. Possible topics include: Markov adaptive methods under uncertainty, Kalman filter, Graphical probabilistic models, General theory of statistical learning, Mixture models, EM algorithm, A very brief introduction to neural networks, Nonparametric regression (local regression, support vector regression, regression trees, MARS, generalized additive models, etc.), Fisher scoring algorithm
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
The students discuss selected advanced probabilistic and statistical methods utilized in the so-called artificial intelligence.
A
23
100.0
B
0
0.0
C
0
0.0
D
0
0.0
E
0
0.0
FX
0
0.0
23
6
133747
B
2-PMS-224
FMFI.KAMŠ/2-PMS-224/22
Applied data science in SAS Viya
2
Evaluation
course
2
26
2, 4
(Single degree study, master II. deg., full time form)
L
1, 2
Mgr. Jozef Kováč, PhD., Mgr. Ivan Kasanický, PhD.
16.02.2026
12.03.2022
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
course
2
26
prezenčná
C
954
mPMS
Probability and Mathematical Statistics
L
2
on-site learning
4
954
mPMS
Probability and Mathematical Statistics
L
1
on-site learning
2
4387
mPMSa
Probability and Mathematical Statistics
L
2
on-site learning
4
4387
mPMSa
Probability and Mathematical Statistics
L
1
on-site learning
2
II.
on-site learning
true
II.
1058242
1
P
lecturer
Jozef
Kováč
Mgr. Jozef Kováč, PhD.
kovac59@uniba.sk
Slovak
1314449
2
P
lecturer
Ivan
Kasanický
Mgr. Ivan Kasanický, PhD.
Slovak
SK
Slovak
Slovak
L
2
4
<_L_>
Recommended literature
Pinheiro C. A. R., Patetta M.: Introduction to Statistical and Machine Learning Methods for Data Science, SAS Institute 2021
<_O_>
Content prerequisite
It is recommended to have completed the courses 2-PMS-222 Programming in SAS and 2-PMS-107 Regression models and to be enrolled in (or have completed) the course 2-PMS-216 Cluster analysis and data classification.
<_PA_>
Conditions for completion of course
Evaluation based on: project
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_PZ_>
Notes
Learning materials will be available in digital form. Enrollment limit: 25 students
<_SO_>
Brief outline of the course
1. Basic overview of the SAS Viya environment. 2. Regression model creation (linear, logistic, GLM, …). 3. Segmentation models and clustering. 4. Basics of practical machine learning (supervised and unsupervised). 5. Comparison of performance of models of different types and best-practice for model quality monitoring.
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
Students will acquire practical knowledge about data processing and various statistical models in SAS Viya.
A
2
33.33
B
3
50.0
C
1
16.67
D
0
0.0
E
0
0.0
FX
0
0.0
6
6
145970
B
2-PMS-225
FMFI.KAMŠ/2-PMS-225/23
Statistical Data Quality
2
Evaluation
seminar
2
26
4
(Single degree study, master II. deg., full time form)
L
2
doc. Mgr. Lenka Filová, PhD., prof. Mgr. Radoslav Harman, PhD.
16.02.2026
16.05.2023
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
seminar
2
26
prezenčná
C
954
mPMS
Probability and Mathematical Statistics
L
2
on-site learning
4
4387
mPMSa
Probability and Mathematical Statistics
L
2
on-site learning
4
II.
on-site learning
true
II.
1014351
1
P
lecturer
Lenka
Filová
doc. Mgr. Lenka Filová, PhD.
filova2@uniba.sk
Slovak
1018338
2
P
lecturer
Radoslav
Harman
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
Slovak
SK
Slovak
Slovak
L
4
<_DI_>
Type, volume, methods and workload of the student - additional information
Type of teaching: seminar
Number of hours per week: 2
Number of hours per level/semester: 28
Form of the course: on-site learning
<_L_>
Recommended literature
Moses B, Gavish L, Vorwerck M: Data Quality Fundamentals: A Practitioner's Guide to Building Trustworthy Data Pipelines, O’Reilly Media, 2022; Enders CK: Applied Missing Data Analysis, The Guildford Press, 2022; The National Academics (collective report): Reproducibility and Replicability in Science, National Academics Press 2019
<_PA_>
Conditions for completion of course
Evaluation based on: talks and reports of students
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
Selection of topics: Missing data (removal and imputation of data), Imbalanced data (reduction and augmentation of data), Reproducibility and replicability of research, Pre-registration of studies, Data consistency, validation and verification, Data quality metrics, Metadata and informative data description, Data archiving
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
Students can understand and apply basic methods control, correction and maintaining of statistical data quality. They also understand the principles and the mathematical background of these methods.
A
10
100.0
B
0
0.0
C
0
0.0
D
0
0.0
E
0
0.0
FX
0
0.0
10
6
133642
B
2-PMS-921
FMFI.KAMŠ/2-PMS-921/22
Diploma Thesis Seminar (2)
3
Evaluation
seminar
3
39
4
(Single degree study, master II. deg., full time form)
L
2
prof. Mgr. Radoslav Harman, PhD., doc. RNDr. Katarína Janková, CSc.
16.02.2026
12.03.2022
FMFI.KAMŠ/2-PMS-126/00
FMFI.KAMŠ/2-PMS-126/00 - Seminar in Mathematical Statistics (2)
2-PMS-126 - Seminar in Mathematical Statistics (2)
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
seminar
3
39
prezenčná
31038
FMFI.KAMŠ/2-PMS-126/00
2-PMS-126
Seminar in Mathematical Statistics (2)
A
954
mPMS
Probability and Mathematical Statistics
L
2
on-site learning
4
4387
mPMSa
Probability and Mathematical Statistics
L
2
on-site learning
4
II.
on-site learning
true
II.
1018338
1
P
lecturer
Radoslav
Harman
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
Slovak
1039018
2
P
lecturer
Katarína
Janková
doc. RNDr. Katarína Janková, CSc.
jankova1@uniba.sk
Slovak
SK
Slovak
Slovak
L
4
<_L_>
Recommended literature
Hofman HA: Scientific Writing and Communication: Papers, Proposals, and Presentations 4th Edition, Oxford University Press 2019; General literature on statistics, according to the needs.
<_PA_>
Conditions for completion of course
Evaluation based on: reports (teaching period)
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
Reports of the students and the lecturer on selected topics of mathematical statistics. Rehearsal of the effective presentation. Discussion on the ethics of the scientific work and publication.
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
The students gained a deeper understanding of selected topics of mathematical statistics. The students have experience with individual and team presentation. The students know the basic tenets of scientific ethics and publication.
A
207
98.1
B
1
0.47
C
3
1.42
D
0
0.0
E
0
0.0
FX
0
0.0
211
6
133646
B
2-PMS-922
FMFI.KAMŠ/2-PMS-922/22
Diploma Thesis Seminar (1)
2
Evaluation
seminar
2
26
3
(Single degree study, master II. deg., full time form)
Z
2
doc. RNDr. Katarína Janková, CSc., prof. Mgr. Radoslav Harman, PhD.
16.02.2026
25.11.2021
FMFI.KAMŠ/2-PMS-125/00
FMFI.KAMŠ/2-PMS-125/00 - Seminar in Mathematical Statistics (1)
2-PMS-125 - Seminar in Mathematical Statistics (1)
8
Person responsible for the delivery, development and quality of the study programme
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
seminar
2
26
prezenčná
31037
FMFI.KAMŠ/2-PMS-125/00
2-PMS-125
Seminar in Mathematical Statistics (1)
A
954
mPMS
Probability and Mathematical Statistics
Z
2
on-site learning
3
4387
mPMSa
Probability and Mathematical Statistics
Z
2
on-site learning
3
II.
on-site learning
true
II.
1039018
1
P
lecturer
Katarína
Janková
doc. RNDr. Katarína Janková, CSc.
jankova1@uniba.sk
Slovak
1018338
2
P
lecturer
Radoslav
Harman
prof. Mgr. Radoslav Harman, PhD.
harman1@uniba.sk
Slovak
SK
Slovak
Slovak
Z
3
<_C_>
Goal
To enlarge the students knowledge of methods of mathematical statistics.
<_L_>
Recommended literature
Anděl, J.: Statistické metody, Matfyzpress Praha 1998
Literature given in the information sheets of the obligatory courses of the study program
<_P_>
Continuous assessment
individual presentation of two topics from the area of mathematical statistics
<_PA_>
Conditions for completion of course
Preliminary semester evaluation: talks
Approximate grade thresholds: A 90%, B 80%, C 70%, D 60%, E 50%
<_PJ_>
Language, which knowledge is needed to pass the course
Slovak, English
<_SO_>
Brief outline of the course
Talks on the topics extending the statistical methods included in the master study program, and the topics of students' diploma theses.
<_URL_>
Course web page URL
http://www.iam.fmph.uniba.sk/ospm/Harman/teaching.htm
<_VH_>
Weighting of course assessment (continuous/final)
100/0
<_VV_>
Learning outcomes
The students will extend their knowledge in selected areas related to the statistical topics included in the master study program. Moreover, the students will improve their presentation skills.
<_Z_>
Final assessment
final thesis
A
205
93.61
B
11
5.02
C
3
1.37
D
0
0.0
E
0
0.0
FX
0
0.0
219
6