Spring 2019 - EE 381V - SPEC TPCS IN MACHINE LEARNING (16725)
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, black-box explainability, algorithmic fairness, adversarial ML, robust statistical modeling, and privacy aware data mining. 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: Diego Garcia-Olano
Schedule and Format
Tuesday/Thursday: 12:30 - 2 ECJ 1.312
- Overview 2 classes
- Explainability (P) 7 classes
- Fairness (P) 6 classes
- Assurance (P) 5 classes
- Guest Speaker 4 classes
- Minor project 1 class (mid March)
- Major project 3 classes (late April)
Topics marked by (P) are student-led presentations. Each such class will cover 2 papers, spending 35 minutes per paper as follows: lead group 20 mins, critiquing group, 5 minutes; discussion 10 mins.
Every alternate class marked (P) will include a 5 minute quiz at the beginning of the class.