- Option A (Literature survey):
- Pick a problem (in ML, NLP or IR) that interests you
- Search the literature for statistical approaches to tackle this problem
- Survey and discuss the relative strengths of each approach
- Option B (Empirical
evaluation):

- Pick a problem (in ML, NLP or IR) that interests you.
- Implement and experiment with several statistical techniques to tackle this problem.
- Option C (Algorithm design):

- Identify a problem (in ML, NLP or IR) for which there are no satisfying approaches.
- Develop a new statistical technique to tackle this problem.
- Analyze theoretically and/or empirically the performance of your technique.
- Option D (Theoretical analysis):

- Identify a problem (in ML, NLP or IR) or statistical technique for which the properties (e.g., complexity, performance) are not well understood.
- Analyze the properties of this problem or technique.

- Submit by October 11

- At most one page
- Which option did you pick?
- Option A (Literature survey):
- What is the problem?
- Cite 10 to 15 papers that you plan to survey.

- Option B (Empirical evaluation):
- What is the problem?
- What statistical techniques do you plan to experiment with?
- Cite 4 to 8 related papers that you plan to survey.
- Option C (Algorithm design):
- What is the problem?
- Why are there no satisfying approaches?
- What is the intuition behind the new technique that you plan to
develop?

- Cite 4 to 8 related papers that you plan to survey.

- Option D (Theoretical analysis):
- What is the problem or technique that you plan to analyze?
- What properties would you like to analyze/prove about this
problem or technique?

- Cite 4 to 8 related papers that you plan to survey.

- 15 minutes presentation + 5 minutes for questions

- Concentrate on the big picture (do not dwell on the details)

- At most 8 pages
**Hand in at the last lecture (Nov 29)**

- Option A (Literature survey):
- Introduction
- What is the problem?
- Why is it an important problem?
- Survey

- Summarize the range of techniques by highlighting their strengths and weaknesses (i.e., the 10-15 papers that you read)
- Tip: this summary should not be a laundry list of techniques with an independent paragraph for each technique
- Suggestion: organize your summary based on desirable
properties of the techniques

- Analysis:
- What is the state of the art?
- Any open problem?
- Conlusion

- What have you learned?

- What future research do you recommend?
- Option B (Empirical evaluation):
- Introduction
- What is the problem?

- Why is it an important 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 the techniques chosen and why

- Empirical evaluation

- Compare empirically the techniques for complexity, performance, ease of use, etc.
- Conclusion:
- What is the best technique?

- Is any technique good enough to declarte the problem solved?
- What future research do you recommend?
- Option C (Algorithm design):
- Introduction
- What is the problem?
- Why can't any of the existing techniques effectively tackle this problem?
- What is the intuition behind the technique that you have
developed?

- Techniques to tackle the problem

- Brief survey of previous work concerning this problem (i.e., the 4-8 papers that you read)
- Describe the technique that you developed
- Brief description of the existing techniques that you will
compare to

- Evaluation

- Analyze and compare (empirically or theoretically) your new approach to existing approaches
- Conclusion:
- Can your new technique effectively tackle the problem?
- What future research do you recommend?
- Option D (Theoretical analysis):
- Introduction
- What is the problem or technique?
- What properties did you analyze/prove about this problem or
technique?

- Analysis

- Brief survey of previous work concerning this problem (i.e., the 4-8 papers that you read)
- Describe the analysis performed

- Conclusion:
- What have you discovered about the technique analyzed?
- What future research do you recommend?