872 search results for “machine learning” in the Public website
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Radiomics-based machine learning classification of bone chondrosarcoma
PhD defence
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Proteins in harmony: Tuning selectivity in early drug discovery
This thesis describes the importance of being able to control the selectivity of potential drug candidates.
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Transforming data into knowledge for intelligent decision-making in early drug discovery
Promotor: A.P.IJzerman Co-promotor: A. Bender
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Sparsity-Based Algorithms for Inverse Problems
Inverse problems are problems where we want to estimate the values of certain parameters of a system given observations of the system. Such problems occur in several areas of science and engineering. Inverse problems are often ill-posed, which means that the observations of the system do not uniquely…
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Learning-based Representations of High-dimensional CAE Models for Automotive Design Optimization
In design optimization problems, engineers typically handcraft design representations based on personal expertise, which leaves a fingerprint of the user experience in the optimization data. Thus, learning this notion of experience as transferrable design features has potential to improve the performance…
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Hunting for the fastest stars in the Milky Way
The high velocity tail of the total velocity distribution of stars provides essential insight into fundamental properties of the Galaxy.
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Interactive scalable condensation of reverse engineered UML class diagrams for software comprehension
Promotores: Prof.dr. J.N. Kok, Prof.dr. M.R.V. Chaudron, Co-Promotor: P. van der Putten
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ECOLE: Experience-based COmputation: Learning to optimisE
Researchers of the Leiden Institute of Advanced Computer Science (LIACS) will develop a training programme the next generation of early stage researchers (ESRs). During a four years project they will be trained to approach industrial challenges in a holistic manner by developing solutions in an automotive…
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2 PhD Candidates, Reinforcement Learning for Sustainable Energy
Science, Leiden Institute of Advanced Computer Science (LIACS)
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Towards High Performance and Efficient Brain Computer Interface Character Speller: Convolutional Neural Network based Methods
A P300-based Brain Computer Interface character speller, also known as P300 speller, has been an important communication pathway, under extensive research, for people who lose motor ability, such as patients with Amyotrophic Lateral Sclerosis or spinal-cord injury because a P300 speller allows human-beings…
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XR (Extended Reality) to learn global challenges
Development of effective VR training for International Law of Armed Conflict (ILAC)
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Yolinde van Paridon
Faculteit der Sociale Wetenschappen
y.van.paridon.3@fsw.leidenuniv.nl | +31 71 527 1488
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To explore the drug space smarter: Artificial intelligence in drug design for G protein-coupled receptors
Over several decades, a variety of computational methods for drug discovery have been proposed and applied in practice. With the accumulation of data and the development of machine learning methods, computational drug design methods have gradually shifted to a new paradigm, i.e. deep learning methods…
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Yingjie Fan
Science
y.fan@liacs.leidenuniv.nl | +31 71 527 4799
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Jian Wang
Faculteit der Sociale Wetenschappen
j.wang@cwts.leidenuniv.nl | +31 71 527 2727
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Webinars
On this page you will find a collection of presentations and videos of the Florence Nightingale Colloquia, seminars at the faculty and other event recordings hosted by the Data Science Research Programme.
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Teachers’ professional learning preferences
How do secondary school teachers’ professional learning preferences relate to teaching experience and the school context?
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Tessa Verhoef: 'An algorithm still has a lot to learn from human interaction'
If an algorithm has to learn to understand language, simply having a lot of data doesn’t help much. Like us, a computer has to learn the language in interaction with others. Tessa Verhoef is fascinated by how this interaction works.
- SAILS Lunch Time Seminar: Machine learning for spatio-temporal datasets + SAILS data observatory
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Babak Rezaeedaryakenari
Faculteit der Sociale Wetenschappen
s.rezaeedaryakenari@fsw.leidenuniv.nl | +31 70 800 3687
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Pascal chair 2023
Peter Flach is Professor of Artificial Intelligence at the University of Bristol. An internationally leading scholar in the evaluation and improvement of machine learning models using ROC analysis and calibration, he has also published on mining highly structured data, on knowledge-driven and explainable…
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Learning labs in conservatoire education
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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Flagships
In CCLS several subgroups have formed, below you can find an overview of these groups with the names of the leading researchers and a short outline of the project.
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Socially Embedded AI Systems
This interdisciplinary research project explores several adaptive machine learning methods which can give insight into the interaction between human and machine. The ultimate goal is open and natural communication between humans and AI that should result in mutual trust, cooperation and coordination…
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Multimodal Data and Machine Learning in the Study of Psychiatric Disorders
PhD defence
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sarcoma and non-sarcoma clinical data with statistical methods and machine learning techniques
PhD defence
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after anterior cervical discectomy: From inferential statistics to Machine Learning
PhD defence
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Developing methods on remote sensing detection of archaeological features in Colombia with LDE grant
A Leiden-Delft-Erasmus research team has been awarded a LDE Global Support Grant to develop reusable algorithms in the remote detection of non-orthogonal architectural features, taking place in the archaeological context of the northern extremities of the Andean, part of the Istmo-Colombian Area.
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Multi-dimensional feature and data mining
In this thesis we explore machine and deep learning approaches that address keychallenges in high dimensional problem areas and also in improving accuracy in wellknown problems. In high dimensional contexts, we have focused on computational fluid dynamics (CFD) simulations.
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Unraveling temporal processes using probabilistic graphical models
Real-life processes are characterized by dynamics involving time. Examples are walking, sleeping, disease progress in medical treatment, and events in a workflow.
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Artifical intelligence gets a boost from quantum computing
Machine learning - on classical computers- has made great progress in the past five years. Computer translation of speech and text is just one example. In Leiden, some researchers expect that machine learning, empowered by quantum systems, even if they only contain a few dozen qubits, can lead to a…
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Nonverbal Learning Disorder (NLD)
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Automated Design and Analysis of Algorithms
The Automated Design and Analysis of Algorithms (ADA) research group pursues the development of Artificial Intelligence techniques that complement, rather than replace, human intelligence.
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Pattern mining for label ranking
Promotor: J.N. Kok, Co-promotor: C.M. Soares, A.J. Knobbe
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Deep learning for visual understanding
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.
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Understanding deep meta-learning
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.
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Sleep and learning in children
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Coffee Machines & Personal Mugs
Have you always wanted to use your own coffee mug at the university coffee machines but it was never accepted? We have good news for you!
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Florence Nightingale Colloquium
Here you can find the recordings of previous Florence Nightingale Colloquia.
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Collaborative learning in higher education: design, implementation and evaluation of group learning activities
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.
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Andreas Sauter
Science
a.w.m.sauter@liacs.leidenuniv.nl | +31 71 527 4799
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Previous SAILS Workshops
SAILS likes to occasionally organise workshops about topics that relate to our programme. On this page you can find more information about previous workshops.
- Data Science & Artificial Intelligence
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Scientific computing for Drug Discovery in Python and/or R
Data analysis with Python and R are rapidly becoming essential skills for modern scientists. Therefore, we are offering courses to develop your scientific computing skills. Those courses are optional for LACDR PhD candidates.
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Exploring deep learning for multimodal understanding
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.
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Collaborative learning in conservatoire education: catalyst for innovation
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.
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Increased striatal activity in adolescence benefits learning
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…
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Professional learning: what teachers want to learn
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?
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Kim Stroet
Faculteit der Sociale Wetenschappen
k.f.a.stroet@fsw.leidenuniv.nl | +31 71 527 3955
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History
Life Sciences Artificial Intelligence Data Science