900 search results for “deep learning” in the Public website
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Ghost in the machine: the deep features of Yanming Guo
In the 1960s at MIT, cognitive scientist Marvin Minsky told a couple of graduate students to program a computer to perform the simple task of recognising objects in pictures, thinking it would be a nice summer project. Scientists from Leiden and the rest of the world are still working on it today.
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Computational speedups and learning separations in quantum machine learning
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…
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Monsters in the Deep: Using simulations to understand the excess baryonic mass in the centres of high-mass, early-type galaxies
This thesis aims to enhance our understanding of galaxies by testing theoretical models of galaxy formation against observations, particularly in the cases of extreme systems which have been found to have an excess of baryonic mass in their central regions, in the form of either supermassive black holes…
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Massively collaborative machine learning
Promotor: J. N. Kok, Co-promotor: A. J. Knobbe
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LIACS Media Lab (LML)
The goal of the LIACS Media Lab (LML) at Leiden University is to conduct state-of-the-art research in the areas of deep learning, artificial intelligence and computer vision.
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Nonverbal Learning Disorder (NLD)
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Computational optimisation of optical projection tomography for 3D image analysis
Optical projection tomography (OPT) is a tomographic 3D imaging technique used for specimens in the millimetre scale.
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Automated detection
The results of the investigations by citizens are used in an innovative research project that investigates the potential of machine learning and automated detection in archaeology.
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Sleep and learning in children
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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 Paraskeva
Science
a.paraskeva@liacs.leidenuniv.nl | +31 71 527 2727
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Andreas Sauter
Science
a.w.m.sauter@liacs.leidenuniv.nl | +31 71 527 4799
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Education
You can do a degree in Artificial Intelligence at Leiden University, but its role is also increasing in other degree programmes.
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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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Siuman Chung
Faculteit der Sociale Wetenschappen
s.chung@fsw.leidenuniv.nl | +31 71 527 3830
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Christine Espin
Faculteit der Sociale Wetenschappen
espinca@fsw.leidenuniv.nl | +31 71 527 6630
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Elise Swart
Faculteit der Sociale Wetenschappen
e.k.swart@fsw.leidenuniv.nl | +31 71 527 2727
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Marit Guda
Faculteit der Sociale Wetenschappen
m.c.guda@fsw.leidenuniv.nl | +31 71 527 6344
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Blended learning
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
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How deep is a mirror?
Light reflects from a mirror, but where exactly does this reflection happen? Well, it depends, Martin van Exter and Corné Koks discovered. Their precise calculations, published in Optics Express, are important for designing optical cavities for quantum communication.
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Learning tools
It is our goal to deliver a convenient, enjoyable, learning experience that goes beyond the basics. All of the apps are an initiative of the HANDS! Lab for Sign Languages and Deaf Studies at Leiden University, as part of the Language Socialization in Deaf Families project funded by the Leiden University…
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Vision and Imaging
On the basis of the characteristic aspects of a picture, certain computers can tell us what the picture is showing. They can learn this in the same way that young children are able to learn to recognize images. Further improving these techniques opens the way to a whole range of new applications. Biology…
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Self-directed learning with mobile technology in higher education
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…
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Collaborative learning in teacher education: Intended, implemented and experienced curriculum
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?
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Student engagement in blended learning in higher education
In what way can teachers support and enlarge student engagement in a blended learning context?
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Archaeology
The Faculty of Archaeology
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Nurbolat Kenbayev
Science
n.kenbayev@liacs.leidenuniv.nl | +31 71 527 2727
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University teachers’ learning paths during technological innovation of education
To what extent are university teachers' individual learning paths influenced by their teaching experience, motivation, and conceptions of teaching and learning through educational technology?
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Research in Physics, Classical/Quantum Information (MSc)
This master’s programme combines Physics with Data Science. You will learn how physics has its own tricks to deal with big data and how techniques from machine learning and deep learning can be applied to classical and quantum data. The first focus of attention is on classical data, including data mining,…
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SewerSense
Scientists of Leiden University and Technical University Delft are going to predict how and where defects in sewer systems arise. They are working with light sensitive camera’s, based on new automated multi-sensor inspection with stereo vision and laser range scanning. Their models are going to process…
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Learning from small samples
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…
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Exploring new methods in comparing sign language corpora
Currently the focus of the project is the development of a tool that utilizes dimensionality reduction techniques in order to analyze and interpret the lexical and phonological variation between different sign languages. Additionally, the application of deep learning techniques for the extraction of…
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Active Learning Network
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.
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Activating teaching and learning
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.
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Flexible learning pathways
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…
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Object-based learning in science museums
How do museum visitors interpret the authenticity of museum objects? How can we support visitors' meaningful interactions with real objects?
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Centre for Professional Learning
The Centre for Professional Learning (CPL) develops in-depth and challenging programmes for higher educated professionals.
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Neurogenomics of vocal learning
How does FoxP1 affect auditory perception on a behavioural and genomic level?
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Leiden Learning & Innovation Centre
LLInC supports innovative and high-quality education, within Leiden University and in partnership with academic and social organisations.
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Beth Lloyd
Faculteit der Sociale Wetenschappen
b.lloyd@fsw.leidenuniv.nl | +31 71 527 2727
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Dineke Tigelaar
ICLON
dtigelaar@iclon.leidenuniv.nl | +31 71 527 6552
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Wilfried Admiraal
ICLON
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Frans Rodenburg
Science
f.j.rodenburg@biology.leidenuniv.nl | +31 71 527 2727
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Language Learning Resource Centre
The language learning resource centre unites all language teaching professionals working at Leiden University: teachers and researchers at the LUCL, ATC, LUCAS, LIAS, and ICLON.
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Learn to Dare!
The ‘Leer te Durven!’ program (Learn to Dare) is a preventive training program for children with mild anxiety symptoms (Simon & Bögels, 2014). The program has been developed for children between the ages of 8 and 12 who feel or behave anxiously, avoid situations, are afraid of doing things wrong, appear…
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LUCDH Workshops
LUCDH facilitates workshops that promote the acquisition of skills and knowledge in employing digital tools.
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Bayesian learning: challenges, limitations and pragmatics
This dissertation is about Bayesian learning from data. How can humans and computers learn from data?