Laboratory, Deep Learning Lab

• Prof. Wolfram Burgard, Prof. Thomas Brox, Prof. Frank Hutter, Asst. Prof. Joschka Boedecker, Dr. Michael Tangermann
• Dr. Abhinav Valada, Niklas Wetzel, Jannik Zürn, Maria Hügle, Aaron Klein, Matilde Gargiani, Arber Zela Christian Zimmermann Silvio Galesso

Welcome to the Deep Learning Lab a joint teaching effort of the Robotics (R), Computer Vision (CV), Machine Learning (ML) and Neurorobotics (NR) 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 want to teach students the practical knowledge that is needed to do research with deep learning and reinforcement learning. 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: Neurorobotics (11LE13P-7320)
Track 3: Computer Vision (11LE13P-7305, 11LE13P-530-15)
Track 4: Automated Machine Learning (11LE13P-7312)

Please fill in this form with your information if you enroll in this course.

Lecture/Exercises: Wednesday, 14.00-16.00 Room: Kinohörsaal/computer pools, Building 082 Beginning: Wednesday, April 24, 2019 Communications: Join our Slack channel for updates on the course (dl-lab-freiburg.slack.com) You can join with this invitation link. Requirements: Fundamental programming skills in Python. Basic knowledge of deep learning, equivalent with having passed the Deep Learning course. Some experience with the Linux toolchain (text editor, compiler, linker, debugger) is recommended. Schedule: Phase I: Lectures 24.04.2019: Lecture 1: Deep Learning for Computer Vision        Hand out Exercise 1 01.05.2019: Holiday, no lecture. Ask open questions on the Slack channel 08.05.2019: Lecture 2: Automated Machine Learning        Exercise 1 submission due and hand out Exercise 2 15.05.2019: Meeting solving open questions 22.05.2019: Lecture 3: Deep Imitation Learning and Deep Reinforcement Learning        Exercise 2 submission due and hand out Exercise 3 29.05.2019: Presentation of topics for final project. Meeting solving open questions Phase II: Project 05.06.2019: Exercise 3 submission due and final project selection due 12.06.2019: Progress discussion 19.06.2019: Project milestone 1 26.06.2019: Progress discussion 03.07.2019: Project milestone 2 10.07.2019: Progress discussion 17.07.2019: Project milestone 3 21.07.2019: Submission Final Project (Poster + Code) 24.07.2019: Final project poster session in Kinohörsaal