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. A collection of papers from selected journals and conferences is provided for the students to choose from. In each meeting, two topics are 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 valentin.bolzspam prevention@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).

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.

ILIAS

The proseminar will cover a wide range of introductory topics in the field of neural networks, such as famous neural network datasets and architectures, most commonly used training strategies, programming frameworks, as well as an overview of many applications such as object recognition, image recognition, regression, etc. A detailed list of topics will be presented in the preliminary meeting.

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. NeurIPS proceedings can be reached here. Also, you can download the PDFs from authors' webpages.

How to read a computer science paper?

• How to Read a Paper
• Reading a Computer Science Research Paper
• How to Read a Computer Science Research Paper