It is our pleasure to invite you to this workshop on the development and application of machine learning methods in Material Science. This internal workshop of the Max-Planck-Research Network on Big-Data-Driven Materials Science connects materials scientists with physicists, machine learning, scientific computing, and data science domain experts. We will discuss cutting-edge examples of machine learning methods to characterize the structure and plasticity of materials, quantify microstructure-material property links, and perform data diagnostics in imaging. Further topics include the discovering of interpretable patterns, structure-property correlations, and causality for such materials data. How to quantify uncertainty in experiments and computer simulations is another key topic field of the workshop. To make these methods, tools, and data open to all of you, there will also be talks and poster presentations on how to build a Materials Encyclopedia with functionally rich metadata in accordance with the FAIR data stewardship principles using open source tools.