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Practical Course: Learning For Self-Driving Cars and Intelligent Systems (10 ECTS)
WS 19, TU München
Organizers: Yuesong Shen, Qadeer Khan, Patrick Wenzel
Assignment to the lab will be done via the matching system (http://docmatching.in.tum.de/).
The preliminary meeting (recommended) to take place on Wednesday, 10 July 2019 at 4pm in HS 2 (00.04.011).
Slides pertaining to the lab will be made available after the preliminary meeting.
Please forward your queries to intellisys-ws19@vision.in.tum.de
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 perception, control, and environmental understanding. This
may also involve working on massive datasets including unstructured or
unordered data.
Pre-requisites
- 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.