Basics of Machine Learning for Non-Computer Scientists

Data is the new oil — you hear this phrase a lot these days. However, similar to oil, data is only valuable if it is used wisely. If data is properly analyzed, evaluated and then interpreted to gain useful insights, then this is the case. Today, machine learning (ML) methods are widely used for this purpose — methods that go beyond statistical evaluations. This workshop will first provide a descriptive theoretical introduction to the basics of machine learning and give you an overview of the most commonly used methods. Using sample data sets, we will show first how data can be analyzed statistically and visually and which types of machine learning methods can be applied to it in order to select, train and apply suitable ML models to new data.

To bring this closer to the participants, we use the no-code software Orange Data Mining in the workshop. The package allows us to implement all steps of a machine-leaning workflow without using a programming language — from building, to training, to applying a model.

To allow direct exchange between participants and instructors, we offer this workshop in face-to-face format rather than as a hybrid course.

The course is taught in English.

You can enroll in this workshop on the University's Qualifications Portal.