Bachelor/Master Thesis: Rule Reduction for Complex Systems like Programming Languages

Background

In the pursuit of unraveling complex systems, there arises a need to simplify the intricate web of rules that often obscures understanding. The task of memorizing countless rules can be tiresome and time-consuming, yet it is often that only a fraction of these rules are essential for a basic grasp of the system. By distilling these core principles, we can make these systems more accessible, optimizing the learning experience and paving the way for a deeper understanding.
Leveraging machine learning techniques to rank and prioritize these rules based on their importance, we can streamline the learning process and maximize knowledge acquisition within the constraints of a limited number of rules. Furthermore, delving into the hierarchy of rules not only enhances comprehension but also might unveil the underlying structure and logic that governs the system.

Tasks

1. Find a good rule-based system, e.g. a programming language (like JavaScript) and get familiar with the utilization
2. Create a machine learning pipeline for ranking rules and evaluating the results
3. Compare different methods (the amount depends on Master or Bachelor Thesis)

Skillset

• Strong Programming skills (preferable Python, knowledge in JavaScript is also a plus)
• The ability to quickly pick up new methods
• At least basic knowledge in machine learning

If you like to have a very interesting topic or just have questions about the topic in any way, please reach out to Alexander Studt (studt@teco.edu) and I can give a more detailed overview of the thesis topic.
If you have your own ideas, which are somewhat similar, you can also pitch it to me and maybe we will find a promising topic together.