Qualification goals

Students will have an understanding of basic applications, concepts and analysis techniques in the area of machine learning for data sciences. They are able to design experiments suitable for complex problems and to collect, access, store, process and analyze data. They know which results can be derived from the respective data and are able to adequately perform and evaluate computer-based procedures in the field of application and in the respective scientific context.

Contents

 Topics from the following areas are covered:

•     Experiment Design
•     Sampling Techniques
•     data cleansing
•     Storage of large data sets
•     Data visualization and graphs
•     Probabilistic data analysis
•     Prediction methods
•     Knowledge discovery
•     Neural networks
•     Support vector machines
•     Reinforcement learning and agent models