Machine learning techniques, especially the ones building on deep neural networks, have been proven extremely useful for tasks like recognizing objects in images, finding clusters in high dimensional datasets, or optimizing stock market investment strategies. In recent years, these techniques have also found multiple applications in various areas of physics research. This seminar will give an overview of these recent developments with a strong bias towards topics in quantum physics. We will explore both the basics of the applied machine learning techniques and the background of the physics topics they have been applied to.
