Computer Science MSc
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"
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Compulsory subjects:
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| ID | course | weekly lecture | weekly practice | type of assesment | credit point | recommended semester | pre-condition |
| GKNM_MSTA025 | Data analysis | 4 | 0 | exam | 4 | 1 | - |
| Digital twins | 2 | 4 | exam | 7 | 1 | - | |
| Numerical linear algebra | 2 | 2 | exam | 5 | 1 | - | |
| Python programming | 2 | 4 | exam | 7 | 1 | - | |
| GKNM_MSTA088 | Introduction to HPC | 2 | 2 | exam | 5 | 1 | - |
| Research methodology | 0 | 2 | mid-term grade | 2 | 1 | - | |
| High performance computing | 2 | 2 | exam | 5 | 2 | Introduction to HPC | |
| Machine learning | 2 | 2 | exam | 5 | 2 | Python programming | |
| Numerical methods for differential equations | 2 | 2 | exam | 5 | 2 | Digital twins, Numerical linear algebra | |
| 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 |
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| ID | course | weekly lecture | weekly practice | type of assesment | credit point | recommended semester | pre-condition |
| Hungarian Language and Culture 1. | 0 | 3 | signature | 0 | 1 | - | |
| Hungarian Language and Culture 2. | 0 | 3 | signature | 0 | 2 | - | |
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Elective subjects:
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| ID | course | weekly lecture | weekly practice | type of assesment | credit point | recommended semester | pre-condition |
| Logic | 2 | 2 | exam | 5 | autumn | - | |
| GKNM_MSTA002 | Theory of algorithms | 2 | 2 | exam | 5 | autumn | - |
| Nonlinear optimization | 2 | 2 | exam | 5 | autumn | - | |
| Web technologies | 2 | 2 | exam | 5 | spring | - | |
| Linear Optimization | 2 | 2 | exam | 5 | spring | - | |
| Model order reduction | 2 | 2 | exam | 5 | autumn | Numerical methods for differential equations | |
| Data assimilation | 2 | 2 | exam | 5 | spring | Data analysis | |
| Selected topics in machine learning | 2 | 2 | exam | 5 | autumn | Machine learning | |
| GKNM_MSTA092 | Production software development | 2 | 2 | exam | 5 | spring/autumn | |
| Digitalization for industry | 2 | 2 | exam | 5 | autumn | - | |
| Sum of elective credit points to be chosen: | 25 | ||||||
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Optional subjects:
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| 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"
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Compulsory subjects:
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| ID | course | weekly lecture | weekly practice | type of assesment | credit point | recommended semester | pre-condition |
| Digital twins | 2 | 4 | exam | 7 | 1 | - | |
| Numerical linear algebra | 2 | 2 | exam | 5 | 1 | - | |
| Nonlinear optimization | 2 | 2 | exam | 5 | 1 | - | |
| Python programming | 2 | 4 | exam | 7 | 1 | - | |
| High performance computing | 2 | 2 | exam | 5 | 1 | - | |
| Machine learning | 2 | 2 | exam | 5 | 2 | Python programming | |
| Web technologies | 2 | 2 | exam | 5 | 2 | Python programming | |
| Project work 1 | 1 | 3 | exam | 6 | 2 | Python programming, Digital twins | |
| Project work 2 | 1 | 3 | exam | 5 | 3 | Project work 1 | |
| Professional Practice | 0 | 0 | signature | 0 | 3 | - | |
| 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: |
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| ID | course | weekly lecture | weekly practice | type of assesment | credit point | recommended semester | pre-condition |
| Hungarian Language and Culture 1. | 0 | 3 | signature | 0 | 1 | - | |
| Hungarian Language and Culture 2. | 0 | 3 | signature | 0 | 2 | - | |
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Elective subjects:
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| ID | course | weekly lecture | weekly practice | type of assesment | credit point | recommended semester | pre-condition |
| Numerical methods for differential equations | 2 | 2 | exam | 5 | 2 | Digital twins, Numerical linear algebra | |
| Linear Optimization | 2 | 2 | exam | 5 | 2 | - | |
| Big Data | 2 | 2 | exam | 5 | 2 | Python programming | |
| Model order reduction | 2 | 2 | exam | 5 | 3 | Numerical methods for differential equations | |
| Data assimilation | 2 | 2 | exam | 5 | 3 | Digital twins | |
| Neural networks | 2 | 2 | exam | 5 | 3 | Machine learning | |
| Selected topics in machine learning | 2 | 2 | exam | 5 | 3 | Machine learning | |
| Cloud computing | 2 | 2 | exam | 5 | 3 | Web technologies | |
| Sum of elective credit points to be chosen: | 25 | ||||||
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Optional subjects:
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| 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. István Harmati |
| Mentor | Dr. Éva Pestiné Rácz |
| 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: |
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For new international students: |
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| Important links for Stipendium Hungaricum scholarship holders |
Frequently asked questions about minimum credit requirement, extension and so on, |
| Schedule of the academic year | |
| Academic information | https://neptun.sze.hu/en_GB/ |