|Instructors||Valentin Bolz, Daniel Weber, Axel Fehrenbach|
|Preliminary Meeting||Mo. 09.11.2020, 16:00 Uhr|
|Credits||3 LP (new PO), 4 LP (old PO)|
|Weekly Meetings||Monday 16:00|
|Room||Online (Link provided in Ilias)|
Deep learning is a subfield of machine learning that has achieved significant state-of-the-art results in many areas of artificial intelligence, including computer vision and robotics, and has been advancing very quickly in recent years. This seminar aims to cover introductory topics in the field of deep learning. It takes shape as a paper reading and discussing the concept of "learning and learning". A collection of papers from selected journals and conferences is provided for the students to choose from. In each meeting, one topic is presented by the students.
Students are graded based on: a) their presentation, b) a short report that they write on the subject, and c) their participation in post-presentation discussions. So, attendance is required to pass the course.
The date for the first meeting can be seen from the table above. In the session, each student chooses one topic and the presentations will start after two weeks; one presentation in each meeting. Participation in the preliminary meeting is required. If you are unable to attend this session, please write to email to axel.fehrenbach. @uni-tuebingen.de
Important note: Since there is a maximum number of 12 participants in this course, please register in ILIAS as soon as possible if you are interesting in taking the seminar.
This is a BSc Seminar (after 5th semester). Interested MSc students are welcome as well.
There are no formal requirements. However, it is helpful to have a good background in mathematics (linear algebra, statistics).
This premilinary list provides an overview of the topics covered in the seminar. Note that this list is not final and will be extended.
Award-Winning Neural Network Architectures
|Comparison of State of the Art Frameworks|
You can get access to the most resources with an online-search from the university network (computer science pools, ZDV pools, VPN-client, etc.). For the literature search, it is recommended to use Google Scholar, Citeseer, arXiv. For very recent submissions on arXiv, click here. If a paper is published in CVPR or ICCV, you can find it on CVF open access. NIPS proceedings can be reached here. Also, you can download the PDFs from authors' webpages.