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IMPORTANT: The location of the summer school has been changed! 

Unfortunately, the Venue at Fanø Vadehavs Center went out of business, and with short notice we had to change venue to Liselund Møde- og Kursussted in Slagelse on Sjælland. This has caused slight changes in the program, and we will not be going on the planned boat trip Tuesday evening to watch seals. Another event is being planned. There will also be slight changes in the bus schedule, because the trip to Slagelse is less than two hours from Copenhagen. The program is not yet updated but will be as soon as we have the information. The scientific program will stay as described.

Welcome to the 2016 summer school on semisupervised learning. This is the eighth in a series of summer school organized jointly by KU and DTU. The summer schools are held in remote locations to encourage interaction between students and teachers. In addition to bringing international expertise in to the groups, the summer schools also provide an important networking opportunity for the students.

Important dates

  • July 1.: Registration is binding
  • July 15.: Registration deadline
  • August 1.: Poster submission deadline
  • August 8.-12.: Summer school

Scientific content

The summer school will consist of 5 days of lectures and exercises. The students will be expected to read a predefined set of scientific articles on machine learning methods prior to the course. Additionally, the students should bring a poster presenting their research field (preferably with an angle towards machine learning in image analysis).

The course will consist of the following parts:

  • A crash course on supervised learning and unsupervised learning.
  • A theoretical insight in the challenges of semi-supervised learning.
  • A practical session with hands-on exercises.
  • Applications of semi-supervised learning in image analysis.

Learning outcomes

After participating in the summer school, the student should

  • Understand semi-supervised learning and be able to differentiate between the different types of models.
  • Have a strong knowledge about the theoretical foundations of semi-supervised learning and its relations to active learning and domain adaptations techniques.
  • Be able to implement basic machine learning from scratch and train them using appropriate initialization and optimization techniques.
  • Be able to apply semi-supervised learning for his/her own research projects.

Link to previous summer schools