Machine Learning (ML) is at the core of many applications of artificial intelligence. A key goal of this course series is to teach the fundamental building blocks behind supervised ML. After a general introduction to the basics of ML in part 1, participants will get in touch with several learning algorithms such as k-nearest neighbors, classification and regression trees, random forests, and deep neural networks in part 2. Finally, we will cover the evaluation and tuning of ML models in depth in part 3.