Coline Devin: By appointment
Time: Tuesday 12:30-1:59pm
Location: Soda 306
Course announcements will be announced through Piazza. If you are in the class, [sign up on Piazza])(https://piazza.com/class/jl5o7zd1s439l).
|08/28||Nicholas Carlini||Main Reading:
|09/04||Trevor Darrell||Main Reading:
|09/11||Coline Devin||Main Reading:
|09/18||Amir Zamir||Main Reading:||Video|
|09/25||Fisher Yu||Main Reading:||Video>|
|10/2||Michael Yartsev: Cancelled, replaced by Lisa Hendricks||Main Reading:
|10/9||David Dohan and Adams yu||Main Reading:||Video|
|10/16||Lydia Liu||Main Reading:||Video|
|10/23||Larry Zitnick||Main Reading:||Video|
|10/30||Chris Olah||Main Reading:
||Not yet available|
|11/06||Richard Zhang||Main Reading:||Not yet available|
|11/13||Michael Yartsev||Main Reading:||Not yet available|
|11/20||No Speaker for Thanksgiving Holiday|
|11/27||Anne Collins||Main Reading:
||Not yet available|
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.
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.
- 1 unit: Participate in reading assignments.
- 2 units: Both reading assignments and a project. Projects may fall into one of
- Distill-like Literature Review of a deep learning topic (e.g., a Distill-like blog post illustrating different optimization techniques used in deep learning)
- Reimplement research code and open source it
- Conference level research project
- You may not take this class for 3 or 4 units.
- Reading assignment deadlines:
- Submit questions about the reading material by Monday noon.
- Project deadlines:
- Project proposal: Friday October 12, 2018, 11:59pm
- Project Milestone: Friday November 16, 2018, 11:59pm
- Final write up: December 10, 2018, 11:59pm
- Project presentations: (Tentative) December 14, 2018
- 20% class participation
- 25% weekly reading assignment
- 55% project
- For students who need computing resources for the class project, we recommend you to look into AWS educate program for students. You’ll get 100 dollar’s worth of sign up credit. Here’s the link .