Machine Learning is a key to develop intelligent systems and analyze data in science and engineering. This course provides an introduction to the fundamental methods at the core of machine learning. It covers theoretical foundations including supervised and unsupervised learning. The theory is beeing introduced by examples from the ambient intellegience domain (and others) including learning as an approach to relize adaptive systems. The implementation of programs in this domain is also part of the course. Having passed this module students are aware of the challenges of machine learning. They have gained basic understanding not just of algorithms but also critical reflection, which allows them to perform problem oriented feature engineering, to find appropriate models.