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Masterpraktikum, Deep Learning Lab


  • Prof. Wolfram Burgard, Prof. Thomas Brox, Prof. Frank Hutter, Asst. Prof. Joschka Boedecker,
  • Andreas Eitel, Abhinav Valada, Maria Hügle Aaron Klein Matilde Gargiani Gabriel Oliveira Tonmoy Saikia

  • 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 two main tracks that focus on different aspects of deep learning research.

    Track 1: Robotics and Reinforcement Learning (R/NR). 11LE13P-7302 and 11LE13P-7320.
    Track 2: Computer Vision and Automated Machine Learning (CV/ML). 11LE13P-7305, 11LE13P-530-15 and 11LE13P-7312.

    Lecture/Exercises:
    Tuesday, 14.00-16.00
    Room: Kinohörsaal/computer pools, Building 082

    Beginning: Tuesday, October 16, 2018

    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 about machine learning techniques (supervised learning, classification, regression), e.g. passed the Deep Learning lecture. Some experience with the Linux toolchain (text editor, compiler, linker, debugger) is recommended.

    Schedule: Phase I: Neural Networks Recap

  • 16.10.2018: Lecture 1: recap neural network basics, hand out Exercise 1
  • 23.10.2018: Meeting solving open questions, final date to switch Tracks (done via email, or best directly via HISinOne)
  • 30.10.2018: Lecture 2: convolutional neural networks, hand out Exercise 2, submission Exercise 1
  • 06.11.2018: Meeting solving open questions

  • Phase II: Split up in 2 Tracks (R/NR) and (CV/ML)

  • 13.11.2018: Lecture 3: Track1: Imitation Learning, Track2: Semantic Segmentation, hand out Exercise 3, submission Exercise 2
  • 20.11.2018: Meeting solving open questions
  • 27.11.2018: Meeting solving open questions
  • 04.12.2018: Lecture 4: TBA, hand out Exercise 4, submission Exercise 3
  • 11.12.2018: Meeting solving open questions
  • 18.12.2018: Presentation topics for Final Project

  • Phase III: Final Project

  • 08.01.2018: Start project work, submission Exercise 4
  • 15.01.2018: Meeting solving open questions
  • 22.01.2018: Meeting solving open questions
  • 29.01.2018: Meeting solving open questions
  • 01.02.2018: submission Final Project (Poster + Code)
  • 05.02.2018: Final project poster session in Kinohörsaal
  • Contact:
    Track 1: eitel@cs.uni-freiburg.de; valada@cs.uni-freiburg.de; hueglem@cs.uni-freiburg.de

    Track 2: kleinaa@cs.uni-freiburg.de; gargiani@cs.uni-freiburg.de; oliveira@cs.uni-freiburg.de; saikiat@cs.uni-freiburg.de

    Slides:
  • Lecture 1: Introduction
  • Lecture 2: CS231 CNNs Intro Hyperparameter Optimization
  • Lecture 3 Track 1: Imitation Learning ,Track2: Semantic Segmentation

  • Assignments: You can find the assignments on github
    Further Materal:
  • The Stanford CS231 deep learning course
  • A great reinforcement learning course by David Silver
  • Berkeley RL course by Sergey Levine
  • Deep learning book by Yoshua Bengio, Ian Goodfellow and Aaron Courville,
  • Deep learning course by Nando de Freitas,


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


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