Course Info

Format:

• There are no exams.
• The course is set up as 5 modules
  1. Introduction (2 classes)
  2. Intro to Privacy (~5 classes)
  3. Intro to Fairness (~5 classes)
  4. Algorithms for Privacy (6 classes)
  5. Algorithms for Fairness (6 classes)
• Modules 1-3: Instructors will lead the discussion with lectures
• Modules 4-5: Students read a paper and lead the discussion in the class.

Graded Student Work: 

• Modules 2 and 3:
  • Each student will independently work on a mini-project/homework assignment.
• Modules 4-5:
  • Each student will individually submit a mini-critique for 10 papers that will be discussed in class. A mini-critique is like a peer-review at a conference and the very least should have:
    • Summary: Motivation + Problem + Approach + Result
    • 3 Strengths
    • 3 Weaknesses
  • 1-2 students will be assigned to lead the discussion of each paper.
• Students will participate in an individual or group research project. Projects can focus on developing new theory/algorithms for privacy/fairness, or on implementing/adapting known algorithms to a real application setting.
• More information about project ideas is forthcoming.

Grading:

• Homework/mini-projects: 20 each
• Mini-critiques: 10
• Class participation: 10
• Project: 40

Standards of Conduct:

Under the Duke Community Standard, you are expected to do your own work–individually for homework/mini-projects, and with your team for the project. We will use the whiteboard policy for homework/mini-projects — you are allowed to discuss solutions with your peers, but you must write submissions/code independently (and indicate in your submission any assistance you received). Any assistance received that is not given proper citation will be considered a violation of the Standard.