Grades:

• 
  

  Class Participation (10%)

  
  


• 
  

  Homework Assignments: (20%)

  
  


• 
  

  Midterm Exam (30%)

  
  


• 
  

  Final Project (40%)

  
  

• 
  

  Proposal: 10%

  
  


• 
  

  Demo: 20%

  
  


• 
  

  Report: 10%

  
  

Textbook/Reference:

1. 
   

   Ze-Nian Li, Mark S. Drew, and Jiangchuan Liu, Fundamentals of Multimedia, 2nd edition, Springer, 2014.

   
   


2. 
   

   Gonzalez and Woods, Digital Image Processing, 3rd edition, Prentice Hall, 2008.

   
   


3. 
   

   Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2007.

   
   


4. 
   

   Richard Szeliski, Computer Vision: Algorithms and Applications, Springer, 2010.

   
   


5. 
   

   Richard J. Radke, Computer Vision for Visual Effects, Cambridge University Press, 2012.

   
   


6. 
   

   S. Wasserman and K. Faust, Social Network Analysis: Methods and Applications, Cambridge University Press, 1994.

   
   


7. 
   

   R. A. Hanneman and M. Riddle, Introduction to Social Network Methods, University of California, 2005.

   
   

http://faculty.ucr.edu/~hanneman/nettext/Introduction_to_Social_Network_Methods.pdf

1. 
   

   R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.

   
   


2. 
   

   Charu C. Aggarwal, Social Network Data Analytics, Springer, 2011.

   
   


3. 
   

   W. Chen, L. V.S. Lakshmanan, and C. Castillo, Information and Influence Propagation in Social Networks, Morgan & Claypool Publishers, 2013.

   
   


4. 
   

   Selected research papers.