Data Mining I

Course overview  

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 the algorithmic foundations of data mining and its applications in computational biology. The course features an introduction to popular data mining problems and algorithms, reaching from classification to clustering. Based on these techniques, we examine how these algorithms can be used to study gene expression, protein function or the structure of biological networks. This course is intended for both students who are interested in applying data mining algorithms and students who would like to gain an understanding of the key algorithmic concepts in data mining.

Current Schedule (Fall 2021)

• Lectures: Wednesdays, from 9 am to 11 am.
  
• Tutorials: Wednesdays, from 8 am to 9 am.
  

Evaluation

• Weekly homework assignments (6 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.