You are here: Home Teaching SS 2019 Deep Learning Lab

Laboratory, Deep Learning Lab

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.

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.

    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

You can find the assignments on github

Further Materal:

Support for this course was generously provided by the Google Cloud Platform Education Grant.

Benutzerspezifische Werkzeuge