Day one of our international summer school Materials 4.0 – Deep Materials: Perspectives on data-driven materials research started with three lectures covering mathematical basics, practical usage and applications of machine learning methods.
The first lecture was given by Prof. Roger French (Case Western University). He introduced the concept of big data and the resulting need for statistical analysis within his field of research on photovoltaics. Collecting massive amounts of data allows him and his team to analyze degradation of solar cells.
In the second lecture, Thomas Lehmann (TU Dresden) gave an overview on some of the available machine-learning tools in Python. By providing a Jupyter notebook the participants could directly try out the respective methods and concepts. The hands-on session in the afternoon gave an opportunity to ask questions and to explore the possibilities offered by Python.
Finally, Dominik Alfke (TU Chemnitz) provided some insights into machine learning from the mathematical perspective involving graphs. His lecture gave interesting links between well-known methods and the properties of the underlying graph-structure of the data.
In the evening, the participants had some time to get to know each other and discuss their research interests, hobbies and everything else over pizza and beverages.