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!

**** UPDATE : Second attempt DL exam ****

The second attempt will be held on 23.06 from 0900-1030 hours at HS-1+2 of the lecture building. Please forward this message to your friends/colleagues who will be taking the exam. 


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)