Instructors

Trevor Darrell
Dawn Song

Teaching Assistants

Lisa Anne Hendricks

Office Hours

Lisa Anne: Monday 5–6:00 pm, Soda-Alcove-283H

Lectures

Time: Monday 1–2:30 pm

Location: 306 Soda

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.

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.

If you are in the class, you may sign up on Piazza. Additionally, you should sign up for the class slack channel and the class google group (this is different than the talk announcement mailing list).

Arxiv Summaries

This semester we started summarizing interesting papers from Arxiv each week. Check out the papers we have chosen and summarized here!

Syllabus

Date Speaker Readings Talk Deadlines
08/28 Anima Anandkumar Main Readings: Background Reading: Jupyter notebooks (credits will be provided on AWS to run them): Role of Tensors in Machine Learning
09/05 Labor Day - No Class
09/11 Vladlen Koltun Main Readings: Background Reading: Learning to Act with Natural Supervision
09/18 Jianfeng Gao Main Readings: Background Reading: Neural approaches to Machine Reading Comprehension and Dialogue Project Proposal Due
09/25 Quoc Le and Barret Zoph Main Reading: Learning Transferable Architectures for ImageNet
10/02 Ross Girshik Main Reading: Background Reading: The Past, Present, and Future of Object Detection
10/09 Igor Mordatch Main Reading: Background Reading: Emergence of Grounded Compositional Language in Multi-Agent Populations
10/16 David Patterson Main Reading: Evaluation of a Domain-Specific Architecture for Deep Neural Networks in the Datacenter: The Google TPU
10/23 Matthew Johnson Main Reading: Background Reading: Composing graphical models and neural networks for structured representations and fast inference
10/30 Percy Liang Main reading: Background Reading: Fighting Black Boxes, Adversaries, and Bugs in Deep Learning Project Milestone Due
11/06 Li Deng Main Reading: Background Reading: From Supervised to Unsupervised Deep Learning: Successes and Challenges
11/13 Rob Fergus Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play by S. Sukhbaatar et al. Unsupervised Learning of Disentangled Representations from Video by E. Dention and V. Birodkar. Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play and Unsupervised Learning of Disentangled Representations from Video
11/20 Rishabh Singh Main Reading: Background Reading: Neural Program Synthesis
11/27 Danny Tarlow Main Reading: Background Reading: Differential Interpreters
11/27 DATE CHANGED! Poster Session 3:00-5:00 (tentative)
12/09 Final 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.

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.

Deadlines

Grading

Additional Notes