About the Program
The curriculum provides knowledge in artificial intelligence (AI) and digital twins that is applicable in many fields of science and industry. The curriculum also offers work experience in high quality industry and international projects for motivated and skilled students during the education, thus prepares students for interdisciplinary professional work and participation in research including PhD programs.
The program starts with mandatory courses on programming, digital twin concepts, machine learning, high-performance computing, web and digitalization technologies, beside some mathematical tools: numerical linear algebra and nonlinear optimization.
Then students pick elective courses to create their own track specialization. Thus they either take
- an artificial intelligence track with artificial neural networks, Big Data technologies, cloud computing,
- a digital twin track with numerical methods for differential equations, data assimilation, model order reduction,
- or any combination of elective courses.
Curriculum
| Course Title | Weekly Hours | Credit Points | Suggested Semester | Type |
|---|---|---|---|---|
| Digital twins | 6 | 7 | 1 | obligatory |
| Numerical linear algebra | 4 | 5 | 1 | |
| Nonlinear optimization | 4 | 5 | 1 | |
| Python programming | 6 | 7 | 1 | |
| High performance computing | 4 | 5 | 1 | |
| Machine learning | 4 | 5 | 2 | obligatory |
| Web technologies | 4 | 5 | 2 | |
| Project work 1 | 4 | 6 | 2 | |
| Numerical methods for differential equations | 4 | 5 | 2 | elective |
| Linear optimization | 4 | 5 | 2 | |
| Big Data | 4 | 5 | 2 | |
| Digitalization for industry | 4 | 5 | 3 | obligatory |
| Project work 2 | 4 | 5 | 3 | |
| Neural networks | 4 | 5 | 3 | elective |
| Selected topics in machine learning | 4 | 5 | 3 | |
| Cloud computing | 4 | 5 | 3 | |
| Model order reduction | 4 | 5 | 3 | |
| Data assimilation | 4 | 5 | 3 | |
| Thesis work | - | 30 | 4 | obligatory |
Contact
Location:
Egyetem ter 1., 9026 Gyor, Hungary
Email:
math@sze.hu