Machine learning is a part of artificial intelligence to recognize patterns and laws on the basis of large amounts of data and intelligent algorithms. Our team mainly deals with Time Series Mining, Deep Learning and Automated Machine Learning. In Automated Machine Learning, we are examining and developing methods for automating algorithm selection and hyper parameter optimization. The goal is to make the complex nature of applying machine learning methods more accessible also for non-experts. We develop Deep Learning architectures of artificial neural networks capable of learning representations, concepts and abstractions from complex data applied on industrial application problems in the context of big data. For this, we consider multi-layer perceptrons, convolutional networks, autoencoders, Boltzman machines, and recurrent networks as basic models. In Time Series Mining, we aim at developing a sound theoretical foundation for a predominalty application-dominated field in order to derive improved classification and clustering methods.