You are here: Home Teaching SS 2024 Laboratory: Deep Learning Lab

Laboratory: Deep Learning Lab

Prof. Abhinav Valada, Prof. Thomas Brox, Prof. Frank Hutter, Prof. Joschka Boedecker

Nick Heppert, Julia Hindel, Arbër Zela, Lennart Purucker, Max Argus, Leonard Sommer, Jan Ole von Hartz, Baohe Zhang,

If you have a question, please post it in the ILIAS forum (so everyone can benefit from the answer). Alternatively, you can also email: To ensure visibility please use [DLL24] <Your Request> as the subject.

Update April 12th: We have updated the schedule to also reflect the Pfingstbreak. Sorry for any inconviences.
Update April 24th: The date of the poster session will be 2nd of August 10am-12pm.

Welcome to the Deep Learning Lab, a joint teaching effort of the Robot Learning (RL), Neurorobotics (NR), Computer Vision (CV) and Machine Learning (ML) 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: Robot Learning (11LE13P-7321)
Track 2: Neurorobotics (11LE13P-7320)
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: Wednesday, 16.00 c.t. -18.00 (Beginning Apr 17, 2024)
Room: 00-006, Building 082
Requirements: Fundamental programming skills in Python. Basic knowledge of deep learning, equivalent to 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: The lab is organized as an in-person event.
Grades: Students need passing grades in all exercises. Final grades are 50% the average exercise grade and 50% of the project grade.


   Phase I: Lectures
  • 17.04.2024: Lecture 1
           Course Overview
           Topic: Deep Imitation and Reinforcement Learning
           Hand out Exercise 1
  • 24.04.2024: Lecture 2
           Topic: Automated Machine Learning
           Q&A; Exercise 1
  • 01.05.2024: Public Holiday: No Lecture
  • 02.05.2024: Exercise 1 submission due
           Hand out Exercise 2
  • 08.05.2024: Lecture 3
           Topic: Computer Vision
           Q&A; Exercise 2
  • 15.05.2024: No lecture
           Exercise 2 submission due
           Hand out Exercise 3
  • 22.05.2024: Pfingstbreak: No Lecture
   Phase II: Project
  • 29.05.2024: Presentation of topics for final project
           Q&A; Exercise 3
           Please fill this form with your project selection.
  • 05.06.2024: Exercise 3 submission due
  • 09.06.2024: Final project selection due
  • 12.06.2024: Final project distribution announcement
           Project kick-off discussion
  • 19.06.2024: Project milestone 1
  • 26.06.2024: Project progress discussion
  • 03.07.2024: Project milestone 2
  • 10.07.2024: Project progress discussion
  • 17.07.2024: Project milestone 3
  • 24.07.2024: Project progress discussion
  • 31.07.2024: Submission Final Project (Code + Poster/Presentation)
  • 02.08.2024: Poster presentations, 10am-12pm



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