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 Tutorial and Speakers

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. 

Tutorial 1: 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.

The tutorial will be presented by Willem Waegeman from Ghent University.

Tutorial 2: Responsible and Explainable Artificial Intelligence

This tutorial presents ongoing research in the field of Responsible Artificial Intelligence (RAI) by introducing core concepts and means of operationalising AI ethics. Attendees will—through both lectures and problem-based learning exercises—get experience in implementing and testing systems for policy compliance.

Here, we introduce the fundamental aspects of RAI by providing a holistic multidisciplinary view. The course structure is such as to introduce to the attendees to the impact intelligent and autonomous systems have on societies and individuals and ongoing state-of-the-art discussions related to ethical, legal, and social aspects of AI. This introduction will be followed by a critical discussion of where accountability and responsibility lie for ethical, legal, and social impacts of these systems, considering decision points throughout the development and deployment pipeline. With this knowledge in mind, students will be introduced to socio-technical approaches the governance, monitoring and control of intelligent systems as tools for incorporating constraints into intelligent system design. Finally, participants apply these skills on a simulated responsible design problem.

The tutorial will be presented by Leila Methnani and Virginia Dignum from Umeå University.

Willem Wageman

Willem Waegeman is a professor at Ghent University, and a member of the research unit Knowledge-based Systems (KERMIT) of the Department Data Analysis and Mathematical Modelling. His main interests are machine learning and data science, including theoretical research and various applications in the life sciences. Specific interests include multi-target prediction problems, constructive machine learning, preference learning and time series analysis.

Leila Methnani

Leila is a WASP-HS affiliated PhD student at the Department of Computing Science and member of the Responsible Artificial Intelligence research group at Umeå University. Her research focuses on explainable AI and human-centric approaches to realising explainable AI in industry. She is an organising committee member of the upcoming workshop in Ethics of Game Artificial Intelligence (EGAI) at ECAI 2023.

Virginia Dignum

Virginia is Professor at Umeå University and Directory of WASP-HS, the Wallenberg Program on Humanities and Society for AI, Autonomous Systems and Software, the largest Swedish national research program on fundamental multidisciplinary research on the societal and human impact of AI. She is a member of the Royal Swedish Academy of Engineering Sciences (IVA), and a Fellow of the European Artificial Intelligence Association (EURAI). She is member of the Global Partnership on AI (GPAI), World Economic Forum’s Global Artificial Intelligence Council, Executive Committee of the IEEE Initiative on Ethically Aligned Design, of ALLAI, the Dutch AI Alliance, EU’s High Level Expert Group on Artificial Intelligence, leader of UNICEF’s guidance for AI and children, and member of UNESCO expert group on the implementation of AI recommendations. She is author of “Responsible Artificial Intelligence: developing and using AI in a responsible way”.

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 (