Computer Science MSc

INFORMATION FOR OUR STUDENTS

Curriculum 2023

Curriculum 2020


Compulsory internship


Thesis


Final exam

HELP AND ORIENTATION FOR NEW STUDENTS

Introduction to the Computer Science Msc programme

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

Welcome guide for Computer Science MSc Students (2023)

Welcome guide for Computer Science MSc Students (2022)


Orientation and first steps for new international students

Travel, visa, residence permit, enrollment, administrative duties, places, dates, regulations and more

https://admissions.sze.hu/orientation

First steps for scholarship holders,
frequently asked questions about minimum credit requirement, extension and so on,
rights and obligations and more

https://stipendiumhungaricum.hu/scholarship-holders/


Orientation and first steps for new Hungarian students

https://felveteli.sze.hu/beiratkozas

ADMISSION INFORMATION 

Why is it worth choosing our Computer Science MSc programme?
What will you study? How can you apply?
You can find all answers, information and useful links here:

https://math.sze.hu/computer-science-msc-admission

CONTACT

Further information: math@sze.hu

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

Consultation with teachers

 

 

 


 

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

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