This course provides an introduction to machine learning with special focus on engineering applications. The course starts with a mathematical background required for machine learning and covers approaches for supervised learning (linear models, kernel methods, decision trees, natural networks), unsupervised learning (clustering, dimensionality reduction) and reinforcement learning as well as theoretical foundations of machine learning (learning theory, optimization).