The students are familiar with different algorithms from the field of machine learning. They have an overview of current machine learning methods (such as SVM, Evolutionary Algorithms, Graphical Models, Convolutional Neural Networks).

Students will be able to use machine learning software libraries to perform prototype implementations and transfer their knowledge of selected methods for implementation in an application.

Students will be able to use evaluation measures specifically to assess the suitability of their implementation for unknown use cases. Students understand the relationship between data preprocessing and data analysis and can selectively choose suitable combinations for an implementation.

The students can specifically deal with technical literature from the field of machine learning and transfer the presented contents to new use cases.