Masterpraktikum, 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.
| ||||||||||||
Slides: | |
Assignments: |
You can find the assignments on github |
Further Materal: |
Support for this course was generously provided by the Google Cloud Platform Education Grant.