* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project
Trent University Department of Computer Science/Studies Data Mining (AMOD 501H) Instructor: Sabine McConnell (firstname.lastname@example.org) Department of Computer Science/Studies OC 102.7 748-1011 ext. 7803 Course Description: 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. Readings Assigned readings will consist of chapters from the book ‘Introduction to Data Mining’ by Tan, Steinbach, and Kumar (Addison-Wesley, 2006), supplemented by relevant journal publications and conference proceedings.