This is a graduate-level seminar course on robust machine learning. In recent years, advances in machine learning have brought forth unprecedented progress in artificial intelligence and predictive data analytics. Despite the empirical success of recent machine learning methods however, there is a growing concern regarding their sensitivity to noise and general instability. The goal of this course is to explore novel techniques that can lead to advances in seemingly unrelated areas of machine learning. These areas include robust classification, stable clustering, as well as reliable network analysis techniques.