Winter School

NLDL 2025 Winter School

The PhD winter school will consist of tutorials by experts in the field and is co-hosted by NORA as part of the NORA Research School. A preliminary program will be made available soon.

Getting formal ECTS Credits:  UiT The Arctic University of Norway awards 5 ECTS for the Winter School for students who register formally for the course at UiT before the 1st of November (for credits, there is a limit of 40 students). To obtain credits, participants will be required to present an ongoing research project (poster presentation) as part of the winter school and complete a home exam afterward. More information on this TBA.

Winter School Speakers and Topics

The NLDL winter school will consists of tutorials from leading expeters in the machine learning feel. Below we present the speakers and the topics of the tutorial they will present.

Aleatoric and Epistemic Uncertainty in Statistics and Machine Learning

Without any doubt, the notion of uncertainty is of major importance in machine learning and constitutes a key element of modern machine learning methodology. In recent years, it has gained in importance due to the increasing relevance of machine learning for practical applications, many of which are coming with safety requirements. In this regard, new problems and challenges have been identified by machine learning scholars, many of which call for novel methodological developments. Indeed, while uncertainty has a long tradition in statistics, and a broad range of useful concepts for representing and quantifying uncertainty have been developed on the basis of probability theory, recent research has gone beyond traditional approaches and also leverages more general formalism and uncertainty calculi.

This tutorial aims to provide an overview of uncertainty quantification in machine learning, a topic that has received increasing attention in the recent past. Starting with a recapitulation of classical statistical concepts, we specifically focus on novel approaches for distinguishing and representing so-called aleatoric and epistemic uncertainty. By the end of the tutorial, attendees will have a comprehensive understanding of the fundamental concepts and recent advances in this field.

Course Description

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 build 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

Contact : 

Winter School Chair: Puneet Sharma (

For other inquiries: Robert Jenssen ( and Michael Kampffmeyer (