DSC291: Trustworthy Machine Learning (SP26)

This course provides a mathematical introduction to trustworthy machine learning and the goal is to prepare interested students to equip themselves with the sufficient background to start doing research in this area.

Course Contents

• Machine Learning and Deep learning basics
• Robust Machine Learning
  • Attacks
  • Verification
  • Defense
• Interpretable Machine Learning
  • Post-hoc Interpretations
  • Interpretable NN Models
  • Evaluations

Course Staff

• Instructor: Prof. Lily Weng, HDSI
• TA: Divyansh Srivastava, HDSI
• TA: Atharv Nair, ECE

For contact information, see this page.

Course Logistics

For course logistics, see this page.