Key areas of the Master’s degree programme
Industrial Data Science and Robotics
Understanding the fundamentals of control engineering, digital signal processing, and inferential statistics is crucial for conducting sophisticated time series analyses and making accurate predictive maintenance forecasts.
AI-based Optimisation and Reinforcement Learning
Modern AI-based optimisation techniques and metaheuristics allow for the optimisation of highly complex systems and discrete problems. Agent-based modelling can be efficiently used for the practical simulation of various scenarios and the optimisation of appropriate reinforcement learning algorithms.
Computational Intelligence
Discovering hidden patterns and relationships in data is central to data mining. Using numerical and statistical methods, as well as the fundamentals of machine learning and neural networks, custom algorithms can be implemented and optimised for these purposes.
Data Storage and Processing
Proficiency in relevant and current scripting languages is an essential qualification in the fields of data science and AI. Additionally, skilled use of query languages and a deep understanding of data storage and transformation are crucial competencies for efficient data processing and storage.
Interdisciplinarity and Practical Applicability
The programme enhances scientific research skills and competencies in modern, practical project management. A special emphasis is placed on sensitivity to ethical issues and data protection. Projects and electives allow for individual specialisation in cutting-edge areas. Through case studies, intensive exchange with companies and project work, on the importance of practical applicability is highlighted.
