Applied Machine Learning
The Applied Machine Learning group aims to develop and apply novel Machine Learning methods for real-world applications. Emerging digitalization allows companies from different fields of industry to produce and collect data from various resources. This is realized by technologies like the Internet of Things (IoT), cyber-physical systems and cloud-computing. All of which is summarized by the fourth industrial revolution, called Industry 4.0, with an increasing demand on research in the area of data analytics. Since these ever-growing amounts of data are difficult to process by conventional methods, machine learning and artificial intelligence provide a powerful and promising approach to handle Big Data. Thus, topics like predictive maintenance, process optimization and process automation benefit from new intelligent algorithms that are developed in the Industry 4.0 environment.
Research areas: Machine Learning, Signal Processing, Wearables, Experimental Studies
Group Head
Group Members
Current Students
- Christopher Kraus
Statistical Modeling for Cooperative Positioning with RSSi data (Bachelor's Thesis) - Daniel Seitz
- Jingna Qiu
Data Augmentation for Electrocardiogram Classication - Kirill Menke
Exploring the Applicability of Mixed Reality for Paramedic Training (Bachelor's Thesis) - Mengyue Wang
CAD2Image: Image Synthesis using CAD Models to Augment Training Data (Master's Thesis) - Pavlo Beylin
- Tassilo Elsberger
Analysis of Artificial Intelligence Trends In German Economics
and Politics - A Data-Driven Approach - Wenyu Zang
Classification of localized defects on silicon carbide (SiC) wafers using domain adaptation techniques
If you are interested in writing a Bachelor’s or Master’s thesis in our group, please check the lab’s Student Theses and Jobs.
Completed Project
- Green Belt ML@Operations
- Theoretical Machine Learning
- Data Mining in the U.S. National Toxicology Program Database
