The NLDL Winter School consists of tutorials by experts in the field and is co-hosted by NORA as part of the NORA Research School.
For registration, please use https://www.nldl.org/attend/registration
Getting formal ECTS Credits: UiT The Arctic University of Norway will award 5 ECTS for the Winter School to students who register formally for the course (the number of spots is limited to 40 students).
To register for the 5 ECTS credits use the following link: https://en.uit.no/admission#kapittel_735916
The course description and the course code are available at: https://uit.no/utdanning/emner/emne/862218/fys-8603
Please note that the application deadline is 15 November, which means that you will receive a confirmation of admission a week after the deadline, i.e., 25 November.
For credits, the participants are required to present an ongoing research project (poster presentation) as part of the winter school and complete a home exam afterward. For students early in their PhD without an ongoing research project can present their PhD research objective and future project as a poster.
Note that the poster presentation is part of the NLDL Winter School and not part of the NLDL proceedings. The posters should be in A0 Portrait format.
Posters can be printed locally via Xtenso. If you want to use this service, the poster should be sent to mette@xtenso.no by 20th December. The cost for poster printing is 750 NOK.
Venue: Auditorium 1 (Teorifagbygget Hus 1, Floor U1) at the UiT Campus
The NLDL Winter School will consist of tutorials from leading experts in the machine learning field. Below we present each tutorial, followed by the tutorial speakers.
Under development
The tutorial will be presented by Mihaela van der Schaar from the University of Cambridge and Anders Boyd from Amsterdam University Medical Centers
Under development
The tutorial will be presented by Aiden Durrant from the University of Aberdeen
Under development
The tutorial will be presented by Kristoffer Wickstrøm, Michael Kampffmeyer and Rwiddhi Chakraborty from UiT The Arctic University of Norway.
TBA
Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM). Mihaela was elected IEEE Fellow in 2009 and Fellow of the Royal Society in 2024. She has received numerous awards, including the Johann Anton Merck Award (2024), the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award. She was a Turing Fellow at The Alan Turing Institute in London between 2016 and 2024. In 2025, she was appointed as Spinoza Guest Professor at Amsterdam University Medical Center. Mihaela is personally credited as inventor on 35 USA patents (the majority of which are listed here), many of which are still frequently cited and adopted in standards. She has made over 45 contributions to international standards for which she received 3 ISO Awards. In 2019, a Nesta report determined that Mihaela was the most-cited female AI researcher in the U.K.
Michael Kampffmeyer is a Professor in the Machine Learning Group at UiT The Arctic University of Norway. He is also a Senior Research Scientist II at the Norwegian Computing Center in Oslo. His research interests include medical image analysis, explainable AI, and learning from limited labels (e.g. clustering, few/zero-shot learning, domain adaptation and self-supervised learning). Kampffmeyer received his PhD degree from UiT in 2018. He has had long-term research stays in the Machine Learning Department at Carnegie Mellon University and the Berlin Center for Machine Learning at the Technical University of Berlin.
Deep learning is a rapidly growing segment of machine learning. It is increasingly used to deliver near-human level accuracy for many tasks such as image classification, voice recognition and natural language processing. Applications areas include facial recognition, scene detection, advanced medical and pharmaceutical research, and autonomous, self-driving vehicles.
This 5-day course is built upon tutorials on specific topics on deep learning. The course further encompasses among others keynote talks, as well as special sessions on industry and diversity in AI as part of the NLDL conference program.
The Winter School will provide a study of several emerging topics of high relevance within advanced deep learning, from a basic understanding of the techniques to the latest state-of-the-art developments in the field. In addition, the participants will be exposed to the latest advances and applications in deep learning by the oral presentations and poster presentations in the main conference program.
The course will thus consist of 5 full days of the NLDL conference including: tutorials, keynote sessions, oral presentations and poster presentations, as well as practical components.
Evaluation Committee for the Course's exam:
Mahdieh Khanmohammadi (University of Stavanger)
Catuscia Palamidessi (INRIA, France)
Christian W. Omlin (University of Agder)
Elisabeth Wetzer (UiT-The Arctic University of Norway)
Gustavo Mello (OsloMet University)
Hans Ekkehard Plesser (Norwegian University of Life Sciences)
Kristoffer Wickstrøm (UiT-The Arctic University of Norway)
Martin Jullum (Norwegian Computing Center)
Nils-Olav Skeie (University of South-Eastern Norway)
Puneet Sharma (UiT-The Arctic University of Norway)
Samia Touileb (University of Bergen)
Sony George (Norwegian University of Science and Technology)
Steven Hicks (Simula)
Tor-Morten Grønli (Christiania University College)
Winter School Chair:
Puneet Sharma (puneet.sharma@uit.no)
Kristoffer Wickstrøm (kristoffer.k.wickstrom@uit.no)
Elisabeth Wetzer (elisabeth.wetzer@uit.no)
For other inquiries:
Robert Jenssen (robert.jenssen@uit.no)
Michael Kampffmeyer (michael.c.kampffmeyer@uit.no).