CS480/680 Fall 2020 - Introduction to Machine Learning

Overview

• Graduate students enrolled in CS680 only
• To be done individually (i.e., no teams)
• 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

Options

• Option A (Literature survey):
  • Pick a problem that interests you
  • Search the literature for machine learning approaches to tackle this problem
  • Survey and discuss the relative strengths of each approach
• Option B (Empirical evaluation):
  • Pick a problem that interests you.
  • Implement and experiment with several machine learning techniques to tackle this problem.
• Option C (Algorithm design):
  • Identify a problem for which there are no satisfying approaches.
  • Develop a new machine learning technique to tackle this problem.
  • Analyze theoretically and/or empirically the performance of your technique.
• Option D (Theoretical analysis):
  • Identify a problem or machine learning technique for which the properties (e.g., complexity, performance) are not well understood.
  • Analyze the properties of this problem or technique.

Proposal (no mark)

• Submit electronically on the LEARN website by November 2 (11:59 pm)
• At most one page (excluding references)
• Which option did you pick?
• Option A (Literature survey):
• What is the problem?
• Cite 6 to 10 papers that you plan to survey.
• Option B (Empirical evaluation):
  • What is the problem?
  • What machine learning techniques do you plan to experiment with?
  • Cite 3 to 6 related papers that you plan to review.
• 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 3 to 6 related papers that you plan to review.
• 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 3 to 6 related papers that you plan to review.

Report (28% of final mark)

• At most 8 pages (excluding references)
• Use the JMLR format: https://www.jmlr.org/format/format.html
• Explain the big picture and any necessary detail
• Submit electronically on the LEARN website by December 18 (11:59 pm)

Suggested Structure for the Report

• 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 6-10 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?
  • Conclusion
    • 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 review of previous work concerning this problem (i.e., the 3-6 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 declare 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 review of previous work concerning this problem (i.e., the 3-6 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 3-6 papers that you read)
    • Describe the analysis performed
  • Conclusion:
    • What have you discovered about the technique analyzed?
    • What future research do you recommend?