Data Mining II

Course overview  

As stated in the overview of the Data Mining I course, the goal of the field of data mining is to find patterns and statistical dependencies in large databases and to gain an understanding of the underlying system from which the data were obtained. In computational biology, data mining contributes to the analysis of vast experimental data generated by high-throughput technologies, and thereby enables the generation of new hypotheses.

In this course, we present advanced topics in data mining and its applications in computational biology, including approaches to analyze structured data such as graphs or sequences, to detect unusual observations in large databases, to take known relations between objects into account in classification, and specialized algorithms and applications of data mining for Biological Network Analysis, Statistical Genetics and Personalized Medicine.

Schedule (Spring 2022)

• Lectures: Wednesdays, from 14:00 to 16:00. In Spring 2022, the course is taught by Juliane Klatt.
  
• Tutorials: Wednesdays, from 16:00 to 17:00 alternating between presenting the solutions to the homework assignment, and the presentation of exercises on topics from the course.
  

Evaluation

• Biweekly homework assignments (5 in total; 30% of the final grade)
• Written final exam (70% of the final grade)

Course prerequisites

• Basic understanding of mathematics, as taught in basic mathematics courses at the Bachelor's level.
• (Recommended) Having completed the Data Mining I course