Predictive Analytics for Structured Data

The course will introduce different tasks of structured output prediction and describe a variety of approaches for solving such tasks. The students will get to know some state-of-the-art tools for solving such tasks and examples of their use in practice. Within the course, the students will learn to apply predictive analytics methods for structured data in the context of their research.

In this course, we will study the different tasks of structured output prediction, such as multi-target classification/regression and (hierarchical) multi-label classification, predictive clustering methods (tree and rule-based) for structured output prediction, ontologies for data mining and their use for describing structured output prediction, ensemble methods for structured output prediction (tree and rule ensembles), applications of structured output prediction to different practical problems from areas such as environmental and life sciences, and image annotation and retrieval. In the practical hands-on work students will be guided through a series of methods for predicting structured outputs. They will analyse relevant data sets (from ecology and systems biology) and that represent different tasks of predicting structured outputs, e.g., multi-target regression, multi-label classification, hierarchical multi-label classification. In the last part of the course, each student will apply and test methods for predicting structured outputs on a selected  relevant doctoral research problem.