This seminar course introduces students with knowledge of machine learning to modern desiderata for trustworthy machine learning, including alignment, fairness, adversarial robustness, privacy, and their interrelations. Students read, present, and discuss seminal and influential recent papers in the field. The course includes a project component aimed at synthesizing the students’ learning.
Spring | Summer | Fall | ||
---|---|---|---|---|
(Session 1) | (Session 2) | |||
2024 | ||||
2023 | ||||
2022 | ||||
2021 | ||||
2020 | ||||
2019 | ||||
2018 | ||||
2017 | ||||
2016 | ||||
2015 | ||||
2014 | ||||
2013 | ||||
2012 | ||||
2011 | ||||
2010 | ||||
2009 | ||||
2008 | ||||
2007 | ||||
2006 | ||||
2005 | ||||
2004 | ||||
2003 | ||||
2002 | ||||
2001 | ||||
2000 | ||||
1999 | ||||
1998 |