Since the amount of available data is steadily increasing, smart data analysis will become more and more important in the future. Machine learning plays a significant role in this context. This course introduces supervised machine learning techniques in a non-technical, hands-on way with integrated exercises and group works.

Among the topics to be discussed in this course are:  the general process of supervised machine learning, sampling and cross-validation techniques, model performance evaluation, ensemble techniques as well as how specific algorithms work (decision trees, random forest, logistic regression, support vector machines, neural networks/deep learning).

The learning objectives of this course are as follows:

• Get familiar with the concept and general analysis process of supervised machine learning and
• Understand the basic theory behind various machine learning techniques.
• Apply different machine learning techniques and evaluate their performance.

Due to the blocked structure and the intensive nature of the course, full attendance is required.

This course integrates various teaching methods such as interactive lectures, class discussions, exercises and group work.

Individual evaluation based multiple-choice tests (70%), performance in online exercises (30%) and participation during lectures and exercises (bonus).

Basic knowledge of standard statistical software packages, such as R (or Python) are recommended. To ensure a shared set of R skills among all participants, we will provide a condensed introduction to R on the first day.