You can ask virtual assistant Siri about the weather, but you can’t have a real conversation with it yet. You can’t refer to anything that’s been said before, or ask the system why it says what it says. Stephan Raaijmakers, Professor by Special Appointment from TNO, hopes to change this.

Artificial intelligence systems are smart. They can recognize patterns better than humans, for example. Yet humans are still very much needed. How can you better steer those AI systems? LIACS lecturer Jan van Rijn wrote a book about this together with a number of colleagues. We asked him a few quest…

Artificial intelligence systems are smart. They can recognize patterns better than humans, for example. Yet humans are still very much needed. How can you better steer those AI systems? LIACS lecturer Jan van Rijn wrote a book about this together with a number of colleagues. We asked him a few quest…

How can we help children and adults to acquire better reading comprehension? Paul van den Broek and his colleagues at the Brain and Education Lab are searching for an answer to this question by investigating reading and the related brain activity.

Bright explosions appear all over the radio and gravitational-wave sky. This dynamic side of the universe which has just been discovered, can be mapped by self-learning machines. The National Science Agenda granted 5 million euro’s to CORTEX, the Center for Optimal, Real-Time Machine Studies of the…

The invention of neural networks marks a critical milestone in the pursuit of true artificial intelligence. Despite their impressive performance on various tasks, these networks face limitations in learning efficiently as they are often trained from scratch.

In this dissertation (titled: Exploring the Synergies between Transfer in Reinforcement Learning and Procedural Content Generation) we explore how the two research fields named in the title, namely Transfer in Reinforcement Learning (TRL) and Procedural Content Generation (PCG) can synergize togethe…

With the dramatic growth of the image data on the web, there is an increasing demand of the algorithms capable of understanding the visual information automatically.

How can humans and computers learn from data? To research this, scientists often use so-called Bayesian methods. Although these methods are widely used, they also have limitations and are not always easy to interpret. In her dissertation Rianne de Heide describes some of these problems and introduces…

How do secondary school teachers’ professional learning preferences relate to teaching experience and the school context?

The aim of this research project was to increase understanding of which collaborative learning approaches already exist in conservatoire education, and how implementation of collaborative learning could be supported.

This thesis investigates the contribution of quantum computers to machine learning, a field called Quantum Machine Learning. Quantum Machine Learning promises innovative perspectives and methods for solving complex problems in machine learning, leveraging the unique capabilities of quantum computers…

Promotor: J. N. Kok, Co-promotor: A. J. Knobbe

The aim of this thesis was to examine what teachers want to learn themselves. The main research question was: what, how and why teachers want to learn? And does this depend on their years of teaching experience and the school at which they work?

Music profession requires strong reflective, collaborative, creative and improvisational skills, yet prevailing one-to-one tuition in conservatoire education focuses mainly on transmission of craft skills. Examining effects of students' collaborative and experiential learning, as in learning labs, creates…

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This dissertation advances automated negotiation by developing agents that can learn and adapt across diverse negotiation settings through three increasingly sophisticated approaches: automated algorithm configuration, portfolio-based strategy selection, and end-to-end reinforcement learning with graph…

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The aim of this study was to provide insight into how teachers in higher education can be supported in the design, implementation and evaluation of group assignments by developing a theoretical and evidence-based framework for the design of group assignments.

Computers are capable of making incredibly accurate predictions on the basis of machine learning. In other words, these computers can learn without intervention once they have been pre-programmed by humans. At LIACS, we explore and push the borders of what a revolutionary new generation of algorithms…

This thesis mainly focuses on multimodal understanding and Visual Question Answering (VQA) via deep learning methods. For technical contributions, this thesis first focuses on improving multimodal fusion schemes via multi-stage vision-language interactions.

The infectious diseases malaria and tuberculosis are responsible for 2.1 million deaths every year. Leiden researchers are currently testing a new tuberculosis vaccine.

This thesis mainly focuses on cross-modal retrieval and single-modal image retrieval via deep learning methods, i.e. by using deep convolutional neural networks.

Heightened activation of the striatum that adolescents show in response to reward is often associated with risk-taking and negative health consequences. This article in Nature Communications investigates a potential positive side of this heightened activation. It shows that the activity peak in late…

The active learning ambition is based on the idea that knowledge is more likely to ‘stick’ when students are actively engaged with their learning and research. This active student participation has implications for how we teach: less consumption of knowledge and more efficient use of contact hours.

The Reinforcement Learning lab conducts research into Reinforcement Learning and Intelligent Combinatorial Algorithms.

The programme is also offered in a blended learning version: this is a combination of distance learning and face-to-face learning. Read more information

Computers are capable of making incredibly accurate predictions on the basis of machine learning. In other words, these computers can learn without intervention once they have been pre-programmed by humans. At LIACS, we explore and push the borders of what a revolutionary new generation of algorithms…

How is collaborative learning in teacher education designed and implemented? How do students experience those collaborative learning assignments? What aspects of the design and the implementation lead to which perceived learning outcomes?

Language learners in higher education increasingly conduct out-of-class self-directed learning facilitated by mobile technology. This project aims to explore how university students use mobile technology for their self-directed language learning and investigate factors that influence their self-directed…

In what way can teachers support and enlarge student engagement in a blended learning context?

In deep reinforcement learning, searching and learning techniques are two important components. They can be used independently and in combination to deal with different problems in AI, and have achieved impressive results in game playing and robotics. These results have inspired research into artificial…

The ambition to have flexible learning pathways is about creating possibilities to improve the content and form of students’ learning process, and to link learning to students’ needs. Students who have access to a flexible range of learning pathways can align their university career with their own personal…

To what extent are university teachers' individual learning paths influenced by their teaching experience, motivation, and conceptions of teaching and learning through educational technology?

Automated Machine Learning (AutoML) is widely used to automatically build a suitable practical Machine Learning (ML) model for an arbitrary real-world problem, reducing the effort of practitioners in the ML development cycle for real-world applications. Optimization is a key part of a typical AutoML…

After this course

Learning from small data sets in machine learning is a crucial challenge, especially when dealing with data imbalances and anomaly detection. This thesis delves into the challenges and methodologies of learning from small datasets in machine learning, with a particular focus on addressing data imbalances…

The active learning network joins together everyone interested in the subject to move the theme further within Leiden University. The SALTSWAT pilot program researches the ways forward for Leiden University.

How do museum visitors interpret the authenticity of museum objects? How can we support visitors' meaningful interactions with real objects?