Skip to main content Link Menu Expand (external link) Document Search Copy Copied

DSC291: Trustworthy Machine Learning (SP23)

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.

Course Contents

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

Course Staff

  • Instructor: Prof. Lily Weng, HDSI
  • TA: Akshay Kulkarni, CSE

For contact information, see this page.

Course Logistics

For course logistics, see this page.