Data Mining

0.5 credits · Graduate

An introduction to the principles of data mining. Topics to be covered include an overview of existing work in data mining with a special focus on applications in astronomy, sampling mechanisms, the statistical foundations of data mining, the problem of missing data, and outlier detection. We will discuss classification techniques such as Support Vector Machines, Neural Networks, and Decision Trees, as well as clustering techniques including k-means, self-organizing maps, and the Expectation Maximization algorithm. Furthermore, the course includes a practical component using open source software. Excludes COIS 4400H.