This course provides a mathematical introduction to trustworthy machine learning and the goal is to prepare interested students to equip themselves with sufficient backgrounds to start doing research in this area.
- Machine Learning and Deep learning basics
- Robust Machine Learning
- Interpretable Machine Learning
- Post-hoc Interpretations
- Interpretable Models
- Instructor: Prof. Lily Weng, HDSI
- TA: Akshay Kulkarni, CSE
For contact information, see this page.
For course logistics, see this page.