Artificial Intelligence has recently achieved big successes in various fields of application such as natural language processing or image recognition. Despite having enormous amounts of parameters, the basic concepts behind those algorithms are fairly simple to understand. This chapter starts with an introduction to the machine learning methodology and its terminology as well as some general metrics to evaluate the quality of an algorithm. It then discusses decision and regression trees as well as their extensions via bagging and boosting as an example to illustrate basic machine learning concepts. The chapter concludes with an introduction to neural networks and explains their basic structure and how to train them.