Vorlesung: Deep Learning for Visual Recognition


  • Dozent(en):
  • Beginn: 10.10.2019
  • Zeiten: Do. 10:15 - 11:45, Meckenheimer Allee 176, Hörsaal 2
  • Veranstaltungsnummer: MA-INF 2313
  • Studiengang: Master
  • Aufwand: 6 CP
  • Prüfungen: 12. Februar 2020, 12:00-15:00, HS 2 und 17 März 2020, 12:00-15:00 HS 2



Neural networks are making a comeback!  Deep learning has taken over the machine learning community by storm, with success both in research and commercially.  Deep learning is applicable over a range of fields such as computer vision, speech recognition, natural language processing, robotics, etc.  This course will introduce the fundamentals of neural networks and then progress to state-of-the-art convolutional and recurrent neural networks as well as their use in applications for visual recognition.  Students will get a chance to learn how to implement and train their own network for visual recognition tasks such as object recognition, image segmentation and caption generation.

No formal pre-requisites.  Students should already be comfortable with concepts in probability theory and optimization and are recommended to have taken at least one course in machine learning or computer vision.  Exercises will be a mix of theory and practical (Python).



Please inscribe yourself into the mailinglist at: externhttps://lists.iai.uni-bonn.de/mailman/listinfo.cgi/vl-dl
In case you have a problem understanding something, questions related to  exercises/projects, please always feel free to write to the mailing list. This should be a place where you students can talk freely about the lecture, so please do not hesitate to ask and reply!


For exercise submissions and course projects, each group is required to have a minimum of 3 students and a maximum of 4. Submission from groups with fewer than 3 students will not be graded.

The exercise sessions will be held on Thursdays as mentioned above (there will be 5 more of them - a total of 6 exercises). Any exception will be notified via the mailing list.

You can submit the theoretical exercises via email to the tutor (as single .pdf file) or to the lecturer during the lecture hours. The solutions will be made available as handouts which you can pick up from Room 3.041. There will be no session explaining the solutions - if you have doubts, please reach out to the tutor. 

For the programming exercise, at least one member from each group is required to come and present the solution. If there is a schedule conflict, please write an email to the tutor for an appointment. There is no need to email the code to the tutor.

** Final Call for Course Projects - This is to notify that all groups working on the course projects have been notified of available GPU slots on the server. Please make sure that you are aware of the slot availability and the final slot selections.

Weitere Dokumente



Übung 1: MLBasics
Übungsblatt  (PDF-Dokument, 170 KB)
Übung 2: MLP
Übungsblatt  (PDF-Dokument, 125 KB)
Übung 3: Optimization Regularization
Übungsblatt  (PDF-Dokument, 101 KB)
Übung 4: CNN
Übungsblatt  (PDF-Dokument, 117 KB)
Übung 5: RNNs
Übungsblatt  (PDF-Dokument, 216 KB)
Übung 6: Autoencoders
Übungsblatt  (PDF-Dokument, 155 KB)