# CS485/685 - Course Project

- Required for graduate students
enrolled in CS685 and optional for undergraduate students enrolled in
CS485

- 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)

- Due date: March 1st

- Some ideas for course
projects

- 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 CS685 and 5% bonus for CS485)

- 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?