Organizers: Qadeer Khan, Prof. Dr. Daniel Cremers

Please forward any queries related to the lab to: intellisys-ws20@vision.in.tum.de

Assignment to the lab is done via the matching system. Please additionally send your application documents to the correspondence email. The preliminary meeting to take place on 13 July 2020, 2pm online on https://bbb.vision.in.tum.de/b/qad-bky-6fu. The preliminary meeting slides can be found here .

In addition to applying to the matching system please remember to also send in your application documents latest by 22 July. See the preliminary meeting slides for more details.

Note that the course goes by the name Lernbasierte Ansätze für autonome Fahrzeuge und intelligente Systeme on the matching system

Learning-based approaches have recently made tremendous progress in the computer vision and robotics research community. In this practical course, we will solve challenging real-world problems in the area of self-driving cars and intelligent systems. Topics will be in the direction of perception, control, and environmental understanding. This may also involve working on massive datasets including unstructured or unordered data.

• Good knowledge of the Python language and basic mathematics such as linear algebra, analysis, and numerics is required.
• Good knowledge of a deep learning framework such as PyTorch, TensorFlow, etc.
• Participation in at least one of the offered deep learning lectures at TUM is required.

Students will work in groups of 2 persons on research oriented projects from the beginning of the semester. Assignment of projects to groups will be done in the first week. There will be biweekly presentations to share results/findings among the groups. To review progress and assist with resolving any issues, students are invited to meet the supervisors on a weekly basis.

Time: Wednesdays 2-4pm
Room: 02.09.023