Machine Learning and Knowledge Extraction (MAKE) (ISSN 2504-4990) is an inter-disciplinary, cross-domain, peer-reviewed, scholarly open access journal to provide a platform to support the international machine learning community.1. Data: data ecosystems, data-preprocessing, data integration, data fusion, data mapping, data generation, and knowledge representation2. Learning: automatic and interactive machine learning methodologies, methods, algorithms and tools, comparisons to human cognition3. Visualization: Data visualization, visual analysis, comparisons to human perception, human-computer interaction4. Privacy: data protection, safety and security, interpretability, transparency, causality, usability, acceptance, ethical, legal and social issues5. Network: Graph-based machine learning, graph data mining, language graphs, probabilistic graphical models6. Topology: Topological data analysis, computational topology, homology, homotopy, persistence, manifolds, simplical complexes7. Entropy: Entropy-based data mining, longitudinal and time dependent data analysis and knowledge discovery