Practical Course: Learning For Self-Driving Cars and Intelligent Systems (10 ECTS)
Winter Semester 2022/23, TU München
Organizers: Qadeer Khan, Mariia Gladkova
Correspondence
Please forward any queries related to the lab to: intellisys-ws21.vision.in@tum.de.
Course Registration
Assignment to the lab is done via the matching system. In addition to applying to the matching system please remember to also send in your application documents latest by 28 July 2022.
The preliminary meeting took place on 18 July 2022. Slides for which are available here.
Note that the course goes by the name Lernbasierte Ansätze für autonome Fahrzeuge und intelligente Systeme on the matching system
Course Content
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 visual localization and mapping, control, and semantic understanding. We will also explore the synergy of learning-based methods with classical geometry-based approaches such as visual odometry and 3D reconstruction. Some applications may also involve working on massive datasets including unstructured or unordered data.
Prerequisites
- Good knowledge of the Python language and basic mathematics such as linear algebra, analysis, probability and numerics etc. is required.
- Good knowledge of a deep learning framework such as PyTorch, TensorFlow, etc.
- Other courses with matching content may be considered. Please highlight this in your application.
Structure
Programming tasks will be given in the initial weeks to get the participants up to speed. Afterwards, students will work in groups of max. 2 persons on research oriented projects. To review progress and assist with resolving any issues, students are invited to meet the supervisors on a weekly basis.
Schedule
Time: Tuesdays 11 am - 1 pm
Room: TBD