Comparing the performance of machine learning algorithms on multiple data sets: frequentist and Bayesian approach.
Learning from data streams: online learning, evaluating model performance on data streams, change detection mechanisms, composing algorithms for machine learning from data streams.
Meta learning: no-free lunch theorem for machine learning, learning about learning, attribute representation of data sets, parametrization of learning algorithms, optimizing the parameter settings of learning algorithms, surrogate models.
Handling background knowledge in machine learning: equation discovery from data and knowledge, relational learning and surrogate models, hierarchically structured background knowledge (taxonomies), background knowledge and (deep) artificial neural networks.
Selected topics in deep learning: handling different objective functions and back propagation, special topologies of deep neural networks (autoencoders, embeddings of unstructured and semi-structured data), semi-supervised learning.