Trevor Darrell
Dawn Song

Teaching Assistants

Robert Nishihara

Office Hours

Robert Nishihara: TBD


Time: Monday 1 - 2:30pm

Location: Soda 306

Room Limit: Soda 306 is designed for smaller courses. We increased course enrollment so more students could benefit from this course. However, if the room becomes too full (and thus poses a fire hazard), students who arrive after the room has reached capacity will be directed to watch the lecture remotely. The link for the live webcast (and recorded lectures) can be found on Piazza.

You may see the intro slides from the first day of class here.


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

For more information about deep learning at Berkeley, sign up for the talk announcement mailing list.

Arxiv Summaries




Course description

In recent years, deep learning has enabled huge progress in many domains including computer vision, speech, NLP, and robotics. It has become the leading solution for many tasks, from winning the ImageNet competition to winning at Go against a world champion. This class is designed to help students develop a deeper understanding of deep learning and explore new research directions and applications of deep learning. It assumes that students already have a basic understanding of deep learning. In particular, we will explore a selected list of new, cutting-edge topics in deep learning, including new techniques and architectures in deep learning, security and privacy issues in deep learning, recent advances in the theoretical and systems aspects of deep learning, and new application domains of deep learning such as autonomous driving.

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