Trevor Darrell
Dawn Song

Teaching Assistants

Robert Nishihara

Office Hours

Robert Nishihara: By appointment


Time: Monday 1 - 2:30pm

Location: Soda 306


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

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Poster Session
Date Speaker Readings Talk Deadlines
01/22 Stefano Soatto Main Reading: Background Reading: The Emergence Theory of Deep Learning
01/29 Alison Gopnik Main Reading: Background Reading: What 4 year olds can do and AI can’t (yet)
02/05 Mike Lewis Main Reading: Background Reading: Deal or No Deal? End-to-End Learning for Negotiation Dialogues
02/12 Kevin Murphy Main Reading: Background Reading: Probabilistic models for vision and language Final Project Proposal Due
02/19 President's Day
02/26 Thomas Funkhouser Main Reading: Background Reading: Data-Driven Methods for Matching, Labeling, and Synthesizing 3D Shapes
03/05 Dileep George Main Reading: Background Reading: Visual Perception, Data Efficiency, and Deep Learning
03/12 Bruno Olshausen Main Reading: Background Reading: Perception in Brains and Machines
03/19 Alex Kurakin Main Reading: Background Reading: Adversarial Examples Final Project Milestone Due
03/26 Spring Break
04/02 Mohammad Norouzi Main Reading: Background Reading: Expressive Structured Models of Images and Objects
04/09 Abhinav Gupta Main Reading: Background Reading: SuperSizing and Empowering Visual Learning
04/16 Ryan Adams Main Reading: Background Reading: Building Probabilistic Structure into Massively Parameterized Models
04/23 POSTER SESSION The poster session is 1-4pm in the Soda Hall 5th floor atrium. You can begin setting up at 12:30pm.

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