Computer Science MSc

Information for our Computer Science MSc students:

Admission information and help for new students:

The Department of Mathematics and Computational Sciences started a new master program in 2020:
Computer Science MSc.

Admission information

Information for our new students

Participation is possible for international students via self-funding or scholarship,
for Hungarian students via self-funding or state-funding.

School year starts each September.

The programme is full-time, 4 semesters and 120 credit points.

Occupation obtained: Computer Scientist (Okleveles programtervező informatikus)

Welcome guide for Computer Science MSc Students (2023)

Welcome guide for Computer Science MSc Students (2022)

How to plan your curriculum? What kind of subjects will you learn?
How can you prepare for exam period?
Tips and tricks for your career and administration

Admission information for international students

Detailed description of the program and admission requirements

How to apply for the program?

Stipendium Hungaricum Scholarship for international students

Scholarship Programme for Christian Young People

Hungarian Diaspora Scholarship

Orientation for new international students

Presentation and information collection about the administrative duties
after your arrival in Hungary

Information for Stipendium Hungaricum Scholarship holders

Admission information for Hungarian students

https://felveteli.sze.hu

Orientation and first steps for Hungarian students

https://felveteli.sze.hu/beiratkozas

Further information: math@sze.hu

Supervisor: Prof. Dr. Zoltán Horváth
Department's Administrator: Szilvia Hegyi

Curriculum 2023

Applies to students starting their studies from academic year 2023/2024 and afterwards
(For the previous curriculum, please scroll down)

You can download the datasheets of the subjects by clicking on the ID and afterwards on "Letöltés"

Compulsory subjects:
ID	*course*	*weekly lecture*	*weekly practice*	*type of assesment*	*credit point*	*recommended semester*	*pre-condition*
GKNM_MSTA025	Data analysis	4	0	exam	4	1	-
GKNM_MSTA035	Digital twins	2	4	exam	7	1	-
GKNM_MSTA036	Numerical linear algebra	2	2	exam	5	1	-
GKNM_MSTA038	Python programming	2	4	exam	7	1	-
GKNM_MSTA088	Introduction to HPC	2	2	exam	5	1	-
GKNM_MSTA089	Research methodology	0	2	mid-term grade	2	1	-
GKNM_MSTA039	High performance computing	2	2	exam	5	2	Introduction to HPC
GKNM_MSTA040	Machine learning	2	2	exam	5	2	Python programming
GKNM_MSTA044	Numerical methods for differential equations	2	2	exam	5	2	Digital twins, Numerical linear algebra
GKNM_MSTA049	Neural networks	2	2	exam	5	3	Machine learning
GKNM_MSTA090	Thesis consultation 1	0	0	mid-term grade	5	3	Research methodology
GKNM_MSTA094	Professional Practice	0	0	signature	0	3	-
GKNM_TATA051	Cloud computing	2	2	mid-term grade	5	3	-
GKNM_MSTA091	Thesis consultation 2	0	0	mid-term grade	25	4	Thesis consultation 2
Sum of compulsory credit points:					85
Hungarian language
ID	*course*	*weekly lecture*	*weekly practice*	*type of assesment*	*credit point*	*recommended semester*	*pre-condition*
KGNB_NOKA036	Hungarian Language and Culture 1.	0	3	signature	0	1	-
KGNB_NOKA037	Hungarian Language and Culture 2.	0	3	signature	0	2	-
Elective subjects:
ID	*course*	*weekly lecture*	*weekly practice*	*type of assesment*	*credit point*	*recommended semester*	*pre-condition*
GKNM_INTA056	Logic	2	2	exam	5	autumn	-
GKNM_MSTA002	Theory of algorithms	2	2	exam	5	autumn	-
GKNM_MSTA037	Nonlinear optimization	2	2	exam	5	autumn	-
GKNM_MSTA041	Web technologies	2	2	exam	5	spring	-
GKNM_MSTA045	Linear Optimization	2	2	exam	5	spring	-
GKNM_MSTA047	Model order reduction	2	2	exam	5	autumn	Numerical methods for differential equations
GKNM_MSTA048	Data assimilation	2	2	exam	5	spring	Data analysis
GKNM_MSTA050	Selected topics in machine learning	2	2	exam	5	autumn	Machine learning
GKNM_MSTA092	Production software development	2	2	exam	5	spring/autumn
GKNM_TATA061	Digitalization for industry	2	2	exam	5	autumn	-
Sum of elective credit points to be chosen:					25
Optional subjects:
ID	*course*	*weekly lecture*	*weekly practice*	*type of assesment*	*credit point*	*recommended semester*	*pre-condition*
AJNM_JFTA005	Computational fluid dinamics in vehicle engineering	0	2	mid-term grade	5	3	Numerical methods for differential equations
AJNM_LSTA024	Logistics	2	2	exam	6	autumn
GKNM_AMTA011	CAE Methods	2	1	exam	5	spring
GKNM_AUTA011	Automatic controls	2	0	exam	5	spring
KGNM_NETA028	Global economics	2	0	exam	4	autumn
KGNM_NETA054	Advanced macroeconomics	2	0	exam	4	spring/autumn
KGNM_VKTA003	Leadership and Organizational Communication	2	2	exam	5	spring/autumn
KGNM_VKTA020	Innovation and Research Communication I.	0	0	mid-term grade	5	spring/autumn
KGNM_VKTA021	Innovation and Research Communication II.	0	0	mid-term grade	5	spring/autumn
Sum of optional credit points to be chosen:					10

Curriculum 2020

Applies to students starting their studies from academic year 2020/2021 until 2022/2023

Download the curriculum in pdf
(Important note: changes are not included in this file, only in the matrix and in the sheet below)

Interactive matrix of subjects