Deep Unsupervised Learning

Registration FAQ

• What topics are covered?

  Generative adversarial networks, variational autoencoders, autoregressive models, flow models, energy based models, compression, self-supervised learning, semi-supervised learning.

• What are the pre-requisites?

  From the original course: significant experience with probability, optimization, deep learning

  For our NUS course iteration, we believe you should also follow the above pre-requisites, where possible. As with many machine learning courses, it would be useful to have basic understanding of linear algebra, machine learning, and probability and statistics. Taking online, open courses on these subjects concurrently or before the course is definitely advisable, if you do not have to requisite understanding. You might try to follow the preflight video lectures, and if these are understandable to you, then you’re all good.

• Is the course chargeable? No, (see caveats for NUS undergraduate students) the course is not chargeable. It is free (as in no-fee). NUS allows us to teach this course for free, as it is not “taught”, per se. Students in the class take charge of the lectures, and complete a project, while the teaching staff facilitates the experience.

• Can I get course credit for taking this? Yes, if you are a first-year School of Computing doctoral student. In this case you need to formally enroll in the course as CS6101, And you will receive one half of the 4-MC pass/fail credit that you would receive for the course, which is a lab rotation course. Even though the left rotation is only for half the semester, such students are encouraged and welcome to complete the entire course.

  Yes, also for NUS undergraduate students (in any faculty). You can enrol in this class through the Do-It-Yourself Module (Group initiated) for 4 MCs. Undergraduate students must attend (physically or virtually) all 13 sessions of the course and complete the video lectures from Prof. Abbeel (in addition to the below requirements). For undergraduate students, we believe that the class is counted towards your maximum workload and is chargeable as a regular module.

  No, for everyone else. By this we mean that no credits, certificate, or any other formal documentation for completing the course will be given to any other participants, inclusive of external registrants and NUS students (both internal and external to the School of Computing). Such participants get the experience of learning deep learning together in a formal study group in developing the camaraderie and network from fellow peer students and the teaching staff.

• What are the requirements for completing the course? Each student must achieve 2 objectives to be deemed to have completed the course:
  • Work with peers to assist in teaching two lecture sessions of the course: One lecture by co-lecturing the subject from new slides that you have prepared a team; and another lecture as a scribe: moderating the Slack channel to add materials for discussion and taking public class notes. All lecture materials by co-lecturers and scribes will be made public.
  • Complete a deep unsupervised learning project. For the project, you only need to use any deep learning framework to execute a problem against a data set. You may choose to replicate previous work from others in scientific papers or data science challenges. Or more challengingly, you may decide to use data from your own context.

• How do external participants take this course? You may come to NUS to participate in the lecture concurrently with all of our local participants. You are also welcome to participate online through Google Hangouts. We typically have a synchronous broadcast to Google Hangouts that is streamed and archived to YouTube.

  During the session where you’re responsible for co-lecturing, you will be expected to come to the class in person.

  As an external participant, you are obligated to complete the course to best your ability. We do not encourage students who are not committed to completing the course to enrol.

Meeting Venue and Time

For both Sessions (I and II): 18:00-20:00, Thursdays at the STMI Executive Classroom (i3 #0-44)

For directions to NUS School of Computing (SoC) and i3: please read the directions here, to park in CP12B and/or take the bus to SoC. and use the floorplan

People

Welcome. If you are an external visitor and would like to join us, please email Kan Min-Yen to be added to the class role. Guests from industry, schools and other far-reaching places in SG welcome, pending space and time logistic limitations. The more, the merrier.

External guests will be listed here in due course once the course has started. Please refer to our Slack after you have been invited for the most up-to-date information.

NUS (Postgraduate, as CS6101): Session I (Weeks 3-7): CAI Qingpeng, SONG Kai, ZHU Fengbin

NUS (Postgraduate, as CS6101): Session II (Weeks 8-13): Dogukan Yigit POLAT, LIANG Yuxuan, Rishav CHOURASIA, WANG Wenjie, WANG Yiwei, WU Zhaomin, XU Cai

NUS (Undergraduates, as DYC1401): ANG Yi Zhe, Eloise LIM, Eugene LIM, Terence NEO, NEW Jun Jie, SHAO Yang

WING: Ibrahim Taha AKSU, HU Hengchang, Min-Yen Kan

External Guests: ANG Shen Ting, Takanori AOKI, Martin KODYŠ, LEE Xin Jie, Joni NGO Thuy Hang, Tram Anh NGUYEN, Harsh SHRIVASTAVA, Chenglei SI, Pardha VISWANADHA, Sunil Kumar YADAV, David YAM