CS-E407513 - Seminar on Deep Learning (4-6 ECTS)
Responsible Teacher: Alexander Ilin
Teachers: Ricardo Falcon Perez, Kate Haitsiukevich, Sam Spilsbury, Nicola Dainese
Level of the Course: Master's and PhD level
Teaching Period: I-II
Description: In this course, we will discuss papers on deep learning and deep reinforcement learning published in this year's ICML, ICLR and NeurIPS. Each student has to
- present one paper (from the list pre-selected by us),
- serve as an opponent in one presentation,
- actively participate in the discussion in slack.
- getting familiar with some of the recent papers from the deep learning literature
- learning to review academic publications
- learning to present scientific works
Registration: There are 12 seminar sessions, two papers will be presented in each session. Therefore, we can accept maximum 24 students to the course. Registration for the course will be prioritized by:
- Study level (PhD, MSc)
- Early registration and selection of the topic.
- Grade in the Deep Learning course.
Prerequisites: CS-E4890 - Deep Learning. This course covers advanced topics and therefore you should be comfortable with the basics.
Highly recommended: ELEC-E8125 - Reinforcement learning. We usually discuss a few papers on RL. If you do not know the basics of RL, it will be difficult for you to discuss those papers. But active participation in the discussion of non-RL
papers should be enough to pass the course.
Grading: Grade from 0 to 5. The grade will be based on your presentation (30%), opponent speech (20%) and participation in the discussion (50%). Full points for participation can be obtained by contributing to the discussion of 50% (12)
of the papers.
Format of the seminars: The seminars are held in person. Two papers are discussed in each session with a 10-minute break between the papers.
The format of discussion of one paper:
- Presentation (max 15 minutes).
- Opponent speech (max 5 minutes).
- Discussion moderated by the teachers.
More information can be found here.
Discussion in slack: The discussion of the papers will happen primarily in slack. During the seminar sessions, we will only briefly summarize the slack discussion. We will also use slack for course announcements. Please join the slack workspace
by following this link.