Fairness, Explainability, and Accountability for ML 2019

Fairness, Explainability, and Accountability for Machine Learning

The course is focused on the ethical implications of applying Big Data and ML tools to socially-sensitive domains, and will present a tool-box of technical solutions for addressing these issues. VVZ Information is available here.
    • The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the course, will officially fail the course.
    • The files are password protected. To obtain the password you need to be inside the ETH network and click here. To establish a VPN connection click here.

Lectures and Office Hours
Lectures Wed 9-11 CAB G 59
Office hours Tue 9-11 CAB F 63.2

Information about course projects can be found here. The project-to-team assignments can be found here. The deadline for project reports is June 6th. The LaTeX template for presentation slides can be found here. The LaTeX template for project reports can be found here.

Performance Assessment
The final grade will be computed based on:
  • 30% written mid-term exam
  • 60% project (40% written report + 20% class presentation)
  • 10% participation in class discussions (10%)