Laboratory: Deep Learning Lab
Prof. Abhinav Valada, Prof. Thomas Brox, Prof. Frank Hutter, Prof. Joschka Boedecker
Co-Organizers:
Markus Käppeler, Jan Ole von Hartz, Alexander Pfefferle , Jake Robertson , Karim Farid, Leonard Sommer, 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:
dl-lab@cs.uni-freiburg.de. To ensure visibility please use
[DLL24] <Your Request> as the subject.
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.
Details
| Lecture/Exercises: |
Wednesday, 16.00 c.t. -18.00 (Beginning Apr 23, 2025) 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. |
Schedule
Phase I: Lectures- 23.04.2025: Lecture 1
Course Overview
Topic: Computer Vision
Hand out Exercise 1 - 30.04.2025: Q&A; Exercise 1
- 07.05.2025: Lecture 2
Topic: Deep Imitation and Reinforcement Learning
Exercise 1 submission due
Hand out Exercise 2 - 14.05.2025: Q&A; Exercise 2
- 21.05.2025: Lecture 3
Topic: Automated Machine Learning
Exercise 2 submission due
Hand out Exercise 3 - 28.05.2025: Q&A; Exercise 3
- 04.06.2025: Exercise 3 submission due
Presentation of topics for final project
Please fill this form (tbd) with your project selection. - 11.06.2025: Final project selection due
Pfingstbreak: No Lecture - 18.06.2025: Final project distribution announcement
Project kick-off discussion - 25.06.2025: Project milestone 1
- 02.07.2025: Project progress discussion
- 09.07.2025: Project milestone 2
- 16.07.2025: Project progress discussion
- 23.07.2025: Project milestone 3
- 30.07.2025: Project progress discussion
- 06.08.2025: Submission Final Project (Code + Poster/Presentation)
- tbd: Poster presentations
Material
Sponsor
Support for this course was generously provided by the Google Cloud Platform Education Grant.
