Trevor Darrell
Dawn Song
Jacob Steinhardt

Teaching Assistants

Samaneh Azadi

Office Hours

Samaneh Azadi: By appointment


Time: Monday 4:00-5:30pm

Location: Soda 306


Course announcements will be announced through Piazza. If you are in the class, [ sign up on Piazza])(


Date Speaker Topic Readings Talk Deadlines
1/28 Jacob Steinhardt course overview/security
  • Slides
  • Video
  • 2/4 Nicolas Papernot security/privacy Main Reading: Background Reading:
  • Slides
  • Video
  • 2/11 Justin Gilmer adversarial examples Main Reading: Background Reading:
  • Slides
  • Video
  • 2/18 Academic Holiday -
    2/25 Stefan Wager causality Main Reading: Background Reading:
  • Slides
  • 3/4 Guillaume Bouchard fake news defense Main Reading: Background Reading:
    3/11 Zachary Lipton explainability Main Reading: Background Reading:
  • Video
  • 3/18 Dustin Tran Bayesian Deep learning Main Reading: Background Reading:
  • Slides
  • Video
  • 3/25 Spring Break -
    4/1 Alex Alemi, Ian Fischer information theory Main Reading: Background Reading:
  • Video
  • 4/8 Been Kim explainability Main Reading: Background Reading:
  • Video
  • 4/15 Balaji Lakshminarayanan uncertainty Main Reading: Background Reading:
  • Video
  • Slides
  • 4/22 Noah Goodman Pyro Main Reading: Background Reading:
  • Video
  • 4/29 Poster Presentation

    Course description

    In recent years, deep learning has enabled huge progress in many domains including computer vision, speech, NLP, and robotics. This class is designed to help students develop a deeper understanding of deep learning and explore new research directions and applications of AI/deep learning and privacy/security. It assumes that students already have a basic understanding of deep learning. In particular, in this semester, we will focus on a theme, trustworthy deep learning, exploring a selected list of new, cutting-edge topics including security and privacy issues in deep learning, explainability, generalization, reliability and robustness, fairness, causality, and theoretical understanding.

    Class format and project

    This is a lecture, discussion, and project oriented class. Each lecture will focus on one of the topics, including a survey of the state-of-the-art in the area and an in-depth discussion of the topic. Each week, students are expected to complete reading assignments before class and participate actively in class discussion.

    Students will also form project groups (two to three people per group) and complete a research-quality class project.

    Enrollment information

    For undergraduates: Please note that this is a graduate-level class. However, with instructors’ permission, we do allow qualified undergraduate students to be in the class. If you are an undergraduate student and would like to enroll in the class, please fill out this form and come to the first lecture of the class. Qualified undergraduates will be given instructor codes to be allowed to register for the class after the first lecture of the class, subject to space availability.

    If you have not received grades for some classes that you are currently enrolled in, please choose Currently Enrolled and then update the form when you receive your final grades. You may also be interested in this class, which is open to undergraduates.

    Students may enroll in this class for variable units.



    Additional Notes