course: Master-Praktikum Introduction to Deep Learning for Computer Vision
- teaching methods:
- practical course
- overhead transparencies
- responsible person:
- Jun. Prof. Dr. Sebastian Houben
- Jun. Prof. Dr. Sebastian Houben (Neuroinformatik)
- offered in:
All statements pertaining to examination modalities (for the summer/winter term of 2020) are given with reservations. Changes due to new requirements from the university will be announced as soon as possible.
|Form of exam:||lab|
|Registration for exam:||Directly with the lecturer|
This one-week hands-on lab course is directed at students in their Master's curriculum and covers basic operations of image processing, machine learning techniques, and end-to-end training of deep convolutional neural networks.
The course focuses on a practical multi-class image classification problem, the recognition of different traffic signs in natural images. Each day is divided into an introduction to the topic and followed by a period of hands-on exercises, which can be prepared in groups of two or three students.
Interested students should be largely familiar with at least one imperative programming language, preferably Python. Basic knowledge of machine learning and computer vision is beneficial, but not strictly required.