Course Content Description
Seminar participants will explore current research trends and established approaches in the Data Science field.
Participants can choose to complete either the theoretical or the practical research project track as part of this seminar.
For the theoretical research project, participants will pick a topic according to their own interests,
or from a pool of suggestions that will be provided.
For their topic, the participants will give an overview of the state-of-the-art relevant to that topic
in a presentation during the seminar (30 min) and a term paper (8 - 10 pages per person, ACM style)
due at the end of the seminar.
Through this process, which the lecturers supervise and guide, the participants will train their ability to:
- find, organize, and systematically read relevant research papers;
- analyze, compare, and contrast research approaches and findings;
- structure, write, and format an academic paper;
- present their work using appropriate presentation techniques and presentation aids;
- answer questions and discuss their work with peers.
The theoretical research project is best suited to compile a state-of-the-art review in preparation for a subsequent
bachelor's or master's thesis in the same area.
For the practical research project, participants will implement a system that solves an applied real-world problem. Participants can suggest a problem or choose from suggestions that will be provided. In addition to delivering a functioning application, completing this seminar requires giving a presentation (30 min) about the project and compiling a developer documentation for the application (min 3 pages ACM style per person).
By completing the practical research track, participants will gain hands-experience with state-of-the-art methods and technologies and train their application development skills.
Topic suggestions for both tracks include, but are not limited to:
- Literature Recommendation
- Collaborator Recommendation
- Semantic Text Analysis
- Analysis of Nontextual Content Features
- Machine Learning Approaches
- Mathematical Content Analysis
- Open Science
- Trusted Timestamping
- Confidential Information Retrieval
- Semantic Analysis of News Articles
- Dissemination of Information
- News Framing Analysis
- Clustering Related News
Artificial Neural Networks for Industrial Applications
- Time Series Analysis for Soft Sensors
- Time Series Forecasting for Predictive Quality Control
- Image Recognition for Waste Product Classification
- Transfer Learning for Simulation and Real-World Data
Explainability of Decision Processes in Artificial Neural Networks
- Object Recognition in Convolutional Neural Networks
- Visualization of Network Activity
- Structure of Learning Representations
- Importance of Network Areas for the Learning Task
By successfully completing the seminar, participants will achieve valuable preparation in terms of the knowledge
and the methodological skills required to successfully complete a bachelor’s or master’s thesis in the groups of
Prof. Meisen and Prof. Gipp.