Assignments during the course consist of 5 tasks
- Reading 2 scientific papers before each discussion session: once a week.
- Write takeaways and questions about each paper read (details here)
- Participate to every discussion session: once a week.
- Presenting and leading the discussion on a scientific paper: twice over the course (details here)
- Completing the programming assignment to generate adversarial examples (details here)
Grading takes 4 components into account
1. Presentation and leading paper discussion (50% of the grade)
- Completeness and relevance of the objective paper presentation
- Quality of the oral speech and of the support for presentation (slides)
- Quality of the critical synthesis
- Quantity and quality of discussion topics
- Ability to engage the audience in the discussion
2. Participation in discussions (15% of the grade)
- Reply to questions/topics launch by discussion leader
- Extend the discussion
- Launch new topics of discussion
3. Writing personal paper takeaways (15% of the grade)
- Submit 1 page summarizing the paper's takeaways in your opinion: what did you learn from this paper? How your perception of ML security changed?
- Submit a few question/discussion topics based on paper reading before each discussion.
- Only submissions are evaluated but not really their content. As long as takeaways and questions related to the paper are submitted, you get full points.
- Submit your assignment before each discussion session (Deadline: 11:55 on discussion day)
4. Completing the programing assignment (20% of the grade)
- Choose a black-box adversarial example method
- Introduce its main concepts
- Implement it
- Perform evaluation and analysis and describe it.