The Deep Learning Lab is divided in three parts:
• First, the students work themselves through an introduction to the Python programming language and all required libraries for the later experiments to obtain some basic knowledge.
• Second, the students will work with certain machine learning methods which are introduced in the Pattern Recognition lecture.
• Third, - in the so-called Machine Learning Challenge - students are required to use their obtained knowledge in order to develop a machine learning system in a competition with the other participating groups. Therefore, the students will be provided with real data which might stem from real-world/industry applications.
To support the ability to work in a team the excercises and the Machine Learning Challenge will be conducted in groups of 3 students. The maximum amount of participants is limited to 30 students. If there are more registrations than places available, we will apply a random selection.
We recommend to have attended either the lecture Pattern Recognition during the winter term or a comparable lecture as a basis for this lab.
The results of the first and the second parts will be reviewed in a colloquium with the supervising assistant. The results of the Machine Learning Challenge will be presented by each group in a closing event.
Lecturer: Prof. Tim Fingscheidt
Assistants: Jasmin Breitenstein, Marvin Klingner
Laboratory (DLL Lab) (ET-NT-111):
Contact hours (SWS): 4h = 5 LPs
Location: R 316 CIP Pool IfN
Kickoff Date: see below
Language: German / English