Course Outline:
Pre-reqs: At least one graduate course completed in Data Mining/Machine Learning. Online courses do not count.
This is an advanced, seminar-oriented course. We shall study recently published papers relevant to the development of responsible and trustworthy data driven automated decision systems. Solid background in pattern recognition/machine learning is assumed. Key topics include building explainable ML models, explanations of decisions made by ML models, interpretation of “black-box” behavior, algorithmic fairness, robustness of solutions specially in response to data/problem drift. Coursework will mainly involve paper presentations, critiques and discussion, a mini coding-based project and a major term project on developing some aspects of a responsible ML system.
- Instructor: Dr. Joydeep Ghosh
- TA: -
(assuming a class of 24 students).
The latest schedule is shared as a spreadsheet via Canvas.
- Overview 2 classes
- Explainability (P) 10-12 classes
- Fairness (P) 5-6 classes
- Assurance (P) 5-6 classes
- Guest Speakers 1-4 classes
- Project talks 3 classes (late Nov)
Topics marked by (P) are student-led presentations, done in groups of 2. By default, one class will cover 2 papers, spending 35 minutes per paper as follows: lead group 20 mins, critiquing group, 5 minutes; discussion 10 mins. On some days we may only have one presentation, specially if there is some left-over discussion to be had.
Papers for Ghosh FTML'22 (as of July 22)
- 3 lead presentations: 30 (group of 2)
- 3 critiques: 15 (group of 2)
- Participation+feedback 20 (individual)
- major project 35 (5 proposal, 10 in-class presentation; 20 written report; group of 2-4))
- Total 100