CS489/CS698 Course Project

• Required for graduate students enrolled in CS698 and optional for undergraduate students enrolled in CS489
  
• Pick an application domain that interests you
• Identify a problem in that application domain
• Analyze or develop new (machine learning) techniques to tackle this problem
• Optional: implement and evaluate empirically some of those techniques
• Undergraduate students can form teams of up to 3 people, but graduate students must do the project by themselves
  

Proposal (no mark)

• At most one page
• Names of team members (for undergraduate students only)
  
• What is the application domain?
• What is the problem you plan to tackle?
• Cite 4-8 papers that you plan to survey concerning this problem.
  
• What (machine learning) techniques do you plan to develop or analyze?

Report (30% of final mark for CS698 and 5% bonus for CS489)

• At most 8 pages
• Explain the big picture and any necessary detail
• Hand in at the final exam
  

Suggested Structure for the Report

• Introduction
• What is the application domain?
• What is the problem?
• Techniques to tackle the problem
  
• brief survey of previous work concerning this problem (i.e., the 4-8 papers that you read)
• brief description of any other relevant technique
• Analysis of techniques
• Comparison: advantages/disadvantages, scalability (time, space and sample complexity), ease of use, etc.
• Optional: report on your empirical evaluation
• Conclusion:
• What is the best technique?
  
• Can we solve the problem today?
• What future research do you recommend?