course: Master-Praktikum Introduction to Deep Learning for Computer Vision

number:
310531
teaching methods:
practical course
media:
overhead transparencies
responsible person:
Jun. Prof. Dr. Sebastian Houben
lecturer:
Jun. Prof. Dr. Sebastian Houben (Neuroinformatik)
language:
english
HWS:
2
CP:
2
offered in:

Exam

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
continual assessment

goals

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.

content

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.

requirements

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.

recommended knowledge

None