Machine Learning for Language and Graphs

The course presents a collection of advanced machine learning topics used in representation learning and explainable artificial intelligence with focus on natural language processing, natural language understanding, knowledge graphs, and multi-relational learning. In particular, it addresses embedding techniques and deep learning approaches for texts and graphs. In this context it also covers ideas from ensemble learning and explainable artificial intelligences. The course covers relevant problems from knowledge graphs, computational linguistics and text mining, e.g., processing of linked data, learning multi-relational data, word sense disambiguation, topic detection, and specifics of morphologically rich languages.

The course requires students to apply machine learning methods on graph and language processing tasks, preferably in the context of their research work.