Course Overview

1. Introduction to Data Mining 
2. Fundamentals of Data Mining 
3. Basic Data Mining Alg. 
4. Supervised Learning 1 (Quiz 1) 
5. Supervised Learning 2 (Release course work) 
6. Supervised Learning 3 
7. Unsupervised Learning (Quiz 2) 
8. Graphical Prob. Models 1 (Course work due; Release Assignment) 
9. Graphical Prob. Models 2 (Assignment due) 
10. Knowledge Graphs (ACEMAP Introduction by Xu) + In-class test 
11. Learning to Rank 
12. Reinforcement Learning 
13. Adversarial Attacks 
14. Privacy-Preserving Data Mining 
15. Course Review (Quiz 3) 
16. Poster Session (Define a data mining problem -> Stating the difference from previous works -> Choosing a model to solve it -> Implementation -> Experimental results -> Report -> Poster)