Event

The lecture gives an overview of approaches of machine learning and data mining for knowledge acquisition from large data sets. These are examined especially with respect to algorithms, applicability to different data representations and the use in real application scenarios.
Knowledge Discovery is an established research area with a large community that investigates methods for discovering patterns and regularities in large amounts of data, including unstructured text. A variety of methods exist to extract patterns and provide previously unknown insights. This information can be predictive or descriptive.
The lecture gives an overview of Knowledge Discovery. Specific techniques and methods, challenges and current and future research topics in this research area will be taught.
Contents of the lecture cover the entire machine learning and data mining process with topics on supervised and unsupervised learning and empirical evaluation. Covered learning methods range from classical approaches like decision trees, support vector machines and neural networks to selected approaches from current research. Learning problems considered include feature vector-based learning and text mining.

Learning obectives:

Students

• know fundamentals of Machine Learning, Data Mining and Knowledge Discovery.
• are able to design, train and evaluate adaptive systems.
• conduct Knowledge Discovery projects in regards to algorithms, representations and applications.

Workload:

• The total workload for this course is approximately 135 hours
• Time of presentness: 45 hours
• Time of preperation and postprocessing: 60 hours
• Exam and exam preperation: 30 hours