The course is organized by Associate Professor Inga Monika Koszalka (

The need for methods to automatically, objectively, and efficiently analyze and interpret data is a common task in many scientific areas. Advances in the field of deep learning have led to a development in machine learning methods such as deep neural networks, which outperform classical data analysis methods in many fields. One of the main reasons of their success is the ability to uncover hidden and complex structures in the data, where layered architectures are employed to extract a deep and rich hierarchical feature representation. Several applied scientific communities such as the remote sensing community started to use deep learning approaches for their application tasks including the identification of objects and forecasting of bio- and geophysical parameters. This illustrates an increasing demand for interdisciplinary approaches that bridge the gap between machine learning and disciplines such as natural sciences. The global scope of this course is to lay the foundations in machine learning and provide necessary deep learning tools in the context of applied sciences. In detail, it includes lectures about fundamental and advanced concepts in neural networks and deep learning, which will be presented with allocated time for discussions. The gained knowledge will be applied in three hands-on sessions covering various practical aspects. The hands-on sessions will cover all necessary aspects of machine learning pipelines that work on real world applications, covering data pre-processing, model learning and testing, as well as quantitative and qualitative evaluation.


For more information on the content, schedule and how to apply for the course, please visit the course web page at: