A very important aspect of this course is the course project. Students will pick an interesting data mining project, and do data mining researches on the topic. At the end of the course, students will learn how to do interesting researches in data mining, and how to write good papers.
Date  Topic  Due  

Sep.  2  Course Introduction  
4  Graph1: basics and diameter (Optional Reading) 

9  Graph2: models for graphs (Mandatory Reading) (Optional Reading) 
Hw1  
11  Graph3: power law (Mandatory Reading) (Optional Reading) 
Hw2  
16  Graph4: structure analysis (Optional Reading) 

18  Spectral analysis1: random walk (Optional Reading) 

23  Spectral analysis2: link analysis (Mandatory Reading) (Optional Reading) 
Hw3  
25  Spectral analysis3: link prediction (Optional Reading) 

30  Spectral Analysis4: triangle counting (Mandatory Reading) (Optional Reading) 
Hw4  
Oct.  2  Project proposal presentation 1.  Proposal due (15:30 pm) 
7  Project proposal presentation 2.  
9  MapReduce1: architecture (Mandatory Reading) 
Hw5 (due: Oct. 11 23:59 pm)  
14  MapReduce2: data mining algorithms (Optional Reading) 

16  MapReduce2: data mining algorithms (cont.) 

21  Midterm week  
23  Midterm week  
28  SVD1: basic definition (Optional Reading) 

30  SVD2: case studies
(Mandatory Reading) (Optional Reading) 
Hw6  
Nov  4  Project progress presentation 1.  
6  Project progress presentation 2.  
11  SVD3: properties (Optional Reading) 

13  Tensor Analysis (Mandatory Reading) (Optional Reading)

Hw7  
18  Approximation (Optional Reading) 

20  Graph compression (Mandatory Reading) (Optional Reading) 
Hw8  
25  Community detection (Optional Reading) 

27  Anomaly detection (Mandatory Reading) (Optional Reading) 
Hw9  
Dec.  2  Project final presentation 1.  
4  Project final presentation 2.  
9  Final exam week  
11  Final exam week 