Laboratory: Deep Learning Lab
Prof. Abhinav Valada, Prof. Wolfram Burgard, Prof. Thomas Brox, Prof. Frank Hutter
Co-Organizers:
Eugenio Chisari, Dr. Daniele Cattaneo, María A. Bravo, Yassine Marrakchi, Fabio Ferreira, Yash Mehta, Arber Zela, Iman Nematollahi, Oier Mees, Dr. Tim Welschehold, Lukas Hermann Shengchao Yan Kshitij Sirohi
Welcome to the Deep Learning Lab a joint teaching effort of the Robotics (R)9, Robot Learning (RL)10, Computer Vision (CV)11, and Machine Learning (ML)12 Labs. Deep learning has brought a revolution to AI research. A good understanding of the principles of deep networks and experience in training them has become one of the main assets for successful research and development of new technology in machine learning, computer vision, and robotics. In this course, we will teach students the practical knowledge that is needed to do research with deep learning, imitation learning, and reinforcement learning. This course consists of a mixture of lectures, exercises and group projects. The course is divided into four tracks that focus on different aspects of deep learning research. Please register for only one of the tracks mentioned below:
Track 1: Robotics (11LE13P-7302)
Track 2: Robot Learning (11LE13P-7321)
Track 3: Computer Vision (11LE13P-7305)
Track 4: Automated Machine Learning (11LE13P-7312)
Please fill in this form with your information if you enroll in this course.
| Lecture/Exercises: | Tuesday, 14.00-16.00 (Beginning Apr 20, 2021) Room: Online via Zoom, Meeting ID: 653 2252 4424, Password: DLlab2021 |
| Requirements: | Fundamental programming skills in Python. Basic knowledge of deep learning, equivalent with having passed the Fundamentals of Deep Learning course. Some experience with the Linux toolchain (text editor, compiler, linker, debugger) is recommended. |
| Lectures, Assignments & Forum: | ILIAS course |
| Remarks: | Due to the Corona crisis, the entire Deep Learning Lab will be held online. This includes the lectures, exercises and group projects. Video lectures and exercises will be uploaded to ILIAS on the day of the lecture. Please watch the lecture and start working on the exercises. You may post questions on the lecture by inserting comments in the video page or post questions about the exercises in the forum. We will then have a Zoom meeting in the week following the lecture where all the questions will be discussed. |
Phase II: Project |
Sponsor
Support for this course was generously provided by the Google Cloud Platform Education Grant.
