Introduction to Machine Learning
Dr. Joschka Bödecker, Dr. Frank Hutter, Dr. Michael Tangermann
Understanding of the basic concepts of machine learning, ability to think on different levels of abstraction, knowledge of exemplary implementations of learning algorithms, ability to independently identify connections of the concepts presented.
Characterization of supervised, unsupervised and reinforcement learning, linear methods for classification and regression, introduction to kernel methods, algorithm independent principles, neural networks, tree-based and committee techniques, statistical learning theory, and subspace decomposition methods.