Computer Science MSc

Admission information

Orientation for new students

Why is it worth choosing our MSc programme?
What will you study? How can you apply?

You can find all answers, information and
useful links here:

Information for prospective students

How to plan your curriculum?
How can you prepare for exam period?

Tips and tricks for your career and
administration:

Welcome guide for curriculum 2023

Welcome guide for curriculum 2020

Curriculum 2023

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

Interactive matrix of subjects for planning individual paths in the curriculum
ttr.sze.hu

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

Interactive matrix of subjects

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_MSTA035	Digital twins	2	4	exam	7	1	-
GKNM_MSTA036	Numerical linear algebra	2	2	exam	5	1	-
GKNM_MSTA037	Nonlinear optimization	2	2	exam	5	1	-
GKNM_MSTA038	Python programming	2	4	exam	7	1	-
GKNM_MSTA039	High performance computing	2	2	exam	5	1	-
GKNM_MSTA040	Machine learning	2	2	exam	5	2	Python programming
GKNM_MSTA041	Web technologies	2	2	exam	5	2	Python programming
GKNM_MSTA042	Project work 1	1	3	exam	6	2	Python programming, Digital twins
GKNM_MSTA043	Project work 2	1	3	exam	5	3	Project work 1
GKNM_MSTA094	Professional Practice	0	0	signature	0	3	-
GKNM_TATA061	Digitalization for industry	2	2	exam	5	3	-
GKNM_MSTA052	Thesis consultation	0	0	mid-term grade	30	4	Project work 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_MSTA044	Numerical methods for differential equations	2	2	exam	5	2	Digital twins, Numerical linear algebra
GKNM_MSTA045	Linear Optimization	2	2	exam	5	2	-
GKNM_MSTA046	Big Data	2	2	exam	5	2	Python programming
GKNM_MSTA047	Model order reduction	2	2	exam	5	3	Numerical methods for differential equations
GKNM_MSTA048	Data assimilation	2	2	exam	5	3	Digital twins
GKNM_MSTA049	Neural networks	2	2	exam	5	3	Machine learning
GKNM_MSTA050	Selected topics in machine learning	2	2	exam	5	3	Machine learning
GKNM_MSTA051	Cloud computing	2	2	exam	5	3	Web technologies
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


Internship	Thesis	Final exam

Contacts
Program supervisor	Dr. Zoltán Horváth
Admission contact	Dr. István Harmati
Tutor responsible for compulsory internship	Dr. Zoltán Horváth
Department responsible for thesis and final exam	Department of Mathematics and Computational Sciences Dr. Zoltán Horváth, head of department Szilvia Hegyi, administrator E-mail: math@sze.hu, Office: C605 Phone number: +36 96 503 464
Academic Registry Office	Enikő Horváth, administrator
International Office	List of colleagues E-mail: international@sze.hu
Tutors	Consultation with tutors of the department University phone book

Useful links
First steps for new students	For new Hungarian students: https://felveteli.sze.hu/beiratkozas
	For new international students: travel, visa, residence permit, enrollment, administrative duties, places, dates, regulations and more https://admissions.sze.hu/orientation
	For Stipendium Hungaricum scholarship holders: frequently asked questions about minimum credit requirement, extension and so on, rights and obligations and more https://stipendiumhungaricum.hu/scholarship-holders/ official website of SZE for Stipendium Hungaricum scholarship: https://admissions.sze.hu/stipendium-hungaricum
Schedule of the academic year	https://munkatars.sze.hu/tanevi-idobeosztas
Academic information	https://neptun.sze.hu/en_GB/

Computer Science MSc

Curriculum 2023

Curriculum 2020

Links

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