Trevor Darrell
Sergey Levine
Dawn Song

Teaching Assistants

Richard Shin
Weicheng Kuo


Time: Monday 1–2:30 pm

Location: 306 Soda

Office hours

Richard: 3-4 PM on Tuesdays in 723 Soda.

Mailing list and Piazza

To get announcements about information about the class including guest speakers, and more generally, deep learning talks at Berkeley, please sign up for the talk announcement mailing list for future announcements.

For students enrolled in the class, please join the Piazza and the student Google Group.


Date Speaker Readings Talk Deadlines
1/23 Devi Parikh No assigned readings for this lecture Visual Question Answering (VQA)
1/30 Richard Socher Tackling the Limits of Deep Learning for NLP
2/6 Rahul Sukthankar Recent Progress on CNNs for Object Detection and Image Compression
2/13 Bryan Catanzaro Scaling Deep Learning Project proposal
2/20 No class (President’s Day)
2/27 Aaron Hertzmann Main reading: Background reading: Artistic Stylization and Geometry Processing
3/6 Kate Saenko Main reading: Background reading: Attentive Captioning without Attention: Designing and understanding LSTM-based captioning models
3/13 Diedrik Kingma Main reading: Optional reading: Unsupervised Deep Learning with Variational Autoencoders: Recent Advances and Applications 1st project milestone
3/20 Jianxiong Xiao Main reading: Learning Affordance for Autonomous Driving
3/27 No class (spring break)
4/3 Le Song Main reading: Background reading: Embedding as a Tool for Algorithm Design
4/10 Alexander Rush Main reading: Background reading: Attention, Seq-to-Seq, and Language Structure 2nd project milestone
4/17 Sanjeev Arora Main reading: Background reading: Generalization and Equilibrium in Generative Adversarial Nets (GANs)
4/24 Bryan Russell Main reading: Background reading: ActionVLAD: Learning spatio-temporal aggregation for action classification
5/1 Poster session
5/10 Final project report due

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.

Please also refer to the course overview slides from the first lecture for more information.

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.

Students may enroll in this class for variable units.

Listing in the Berkeley Academic Guide. Class # is 34001.



Additional Notes