1,348 search results for “machine learning” in the Public website
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Machine Learning
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…
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Massively collaborative machine learning
Promotor: J. N. Kok, Co-promotor: A. J. Knobbe
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Artificial Intelligence & Machine Learning
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…
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Predicting alcohol use disorder through machine learning
How to come to valid risk stratification of alcohol use disorder?
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Machine learning for radio galaxy morphology analysis
We explored how to morphologically classify well-resolved jetted radio-loud active galactic nuclei (RLAGN) in the LOw Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) using machine learning.
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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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Efficient Tuning of Automated Machine Learning Pipelines
PhD defence
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Reliable and Fair Machine Learning for Risk Assessment
The focus of this thesis is on the technical methods which help promote the movement towards Trustworthy AI, specifically within the Inspectorate of the Netherlands.
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Machine learning and computer vision for urban drainage inspections
Sewer pipes are an essential infrastructure in modern society and their proper operation is important for public health. To keep sewer pipes operational as much as possible, periodical inspections for defects are performed.
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Postdoc on Causal Machine Learning for Spatio-temporal Datasets
Science, Leiden Institute of Advanced Computer Science (LIACS)
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Automated machine learning for dynamic energy management using time-series data
Time-series forecasting through modelling sequences of temporally dependent observations has many industrial and scientific applications. While machine learning models have been widely used to create time-series forecasting models, creating efficient and performant time-series forecasting models is…
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of post-translationally modified peptides in Streptomyces with machine learning
The ongoing increase in antimicrobial resistance combined with the low discovery of novel antibiotics is a serious threat to our health care.
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Data-Driven Machine Learning and Optimization Pipelines for Real- World Applications
Machine Learning is becoming a more and more substantial technology for industry.
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Quantum machine learning: on the design, trainability and noise-robustness of near-term algorithms
This thesis addresses questions on effectively using variational quantum circuits for machine learning tasks.
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PhD candidate, Feature-based machine learning for precision diagnosis of neuromuscular diseases
Science, Leiden Institute of Advanced Computer Science (LIACS)
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2 Lecturer positions (0.6-1.0 FTE) in Computer Science, Machine Learning, and AI
Science, Leiden Institute of Advanced Computer Science (LIACS)
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Information-theoretic Partition-based Models for Interpretable Machine Learning
PhD defence
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The flux and flow of data: connecting large datasets with machine learning in a drug discovery envirionment
This thesis focuses on data found in the field of computational drug discovery. New insight can be obtained by applying machine learning in various ways and in a variety of domains. Two studies delved into the application of proteochemometrics (PCM), a machine learning technique that can be used to…
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Machine learning-based NO2 estimation from seagoing ships using TROPOMI/S5P satellite data
The marine shipping industry is one of the strongest emitters of nitrogen oxides (NOx), a pollutant detrimental to ecology and human health. Over the last 20 years, the pollution produced by power plants, the industry sector, and cars has been decreasing.
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Andreas ParaskevaScience
a.paraskeva@liacs.leidenuniv.nl | +31 71 527 2727
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Machine learning predicts preferences
Cláudio de Sá predicted the preferences of people using rankings. He adjusted ‘classical’ machine learning approaches, making them suitable for predicting preferences. His work can be applied in the prediction of election results. PhD defence on 16 December.
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PNAS Paper Prize for quantum machine learning
‘We hope our paper highlights the possibilities and benefits of including artificial intelligence in quantum physics to do new discoveries.’ Vedran Dunjko of the Leiden Institute of Advanced Computer Science contributed to a paper that was published in PNAS last year and now received a Cozzarelli Prize…
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Novel system-inspired model-based quantum machine learning algorithm for prediction and generation of High-Energy Physics data
Assistant Professor Vedran Dunjko and his team received a gift from Google to support their quantum research. The research focuses on whether quantum computers can provide new ways of understanding the mysteries of high-energy physics.
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Frans RodenburgScience
f.j.rodenburg@biology.leidenuniv.nl | +31 71 527 2727
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Wouter van LoonFaculteit der Sociale Wetenschappen
w.s.van.loon@fsw.leidenuniv.nl | +31 71 527 2727
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Optimally weighted ensembles of surrogate models for sequential parameter optimization
It is a common technique in global optimization with expensive black-box functions to learn a surrogate-model of the response function from past evaluations and use it to decide on the location of future evaluations.
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Machine Learning Improves Cross-border Tax Estimates
Multidisciplinary research has established that VAT-results are in practice six times lower than what it should have been. The new estimates rely on machine learning techniques.
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Alumnus Robert Ietswaart: ‘Machine learning is revolutionising drug discovery’
Robert Ietswaart does research into gene regulation at the famous Harvard Medical School in Boston. He developed an algorithm to better predict whether a candidate medicine is going to produce side effects. He studied mathematics and physics in Leiden, and gained his PhD in computational biology in…
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Fatemeh Mehrafrooz MayvanScience
f.mehrafrooz.mayvan@liacs.leidenuniv.nl | +31 71 527 2727
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Simon MarshallScience
s.c.marshall@liacs.leidenuniv.nl | +31 71 527 2727
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I-Fan LinScience
i.lin@liacs.leidenuniv.nl | +31 71 527 2727
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Philipp KropfScience
p.kropf@cml.leidenuniv.nl | +31 71 527 2727
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Modelling the interactions of advanced micro- and nanoparticles with novel entities
Novel entities may pose risks to humans and the environment. The small particle size and relatively large surface area of micro- and nanoparticles (MNPs) make them capable of adsorbing other novel entities, leading to the formation of aggregated contamination.
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Julia WasalaScience
j.wasala@liacs.leidenuniv.nl | +31 71 527 4799
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Data-driven donation strategies: understanding and predicting blood donor deferral
The research in this dissertation aims to optimise blood donation processes in the framework of the Dutch national blood bank Sanquin. The primary health risk for blood donors is iron deficiency, which is evaluated based on donors' hemoglobin and ferritin levels.
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Nurbolat KenbayevScience
n.kenbayev@liacs.leidenuniv.nl | +31 71 527 2727
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The Use of Machine Learning in Public Organizations - an Interview with PhD Student Friso Selten
Friso Selten recently started a PhD position that is part of the SAILS program. This PhD project is a collaboration between FGGA, LIACS, and eLaw, and is supervised by Bram Klievink (FGGA), Joost Broekens (LIACS), and Francien Deschene (eLaw). In the project Friso will investigate the influence of artificial…
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The BIAS project at the Applied Machine Learning Days in Lausanne, Switzerland
The Applied Machine Learning Days AMLD is a global platform that brings together experts and participants from over 40 countries across industry, academia, and government in the field of Machine Learning. In this year’s edition, members of the BIAS project organized a track around the topic Fairness…
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Exploring big data approaches in the context of early stage clinical
Als gevolg van de grote technologische vooruitgang in de gezondheidszorg worden in toenemende mate gegevens verzameld tijdens de uitvoering van klinische onderzoeken.
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Network analysis methods for smart inspection in the transport domain
Transport inspectorates are looking for novel methods to identify dangerous behavior, ultimately to reduce risks associated to the movements of people and goods. We explore a data-driven approach to arrive at smart inspections of vehicles.
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Algorithm selection and configuration for Noisy Intermediate Scale Quantum methods for industrial applications
Quantum hardware comes with a different computing paradigm and new ways to tackle applications. Much effort has to be put into understanding how to leverage this technology to give real-world advantages in areas of interest for industries such as combinatorial optimization or machine learning.
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Michael LewScience
m.s.lew@liacs.leidenuniv.nl | +31 71 527 7034
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Numerical exploration of statistical physics
In this thesis, we examine various systems through the lens of several numerical methods.
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Data-Driven Risk Assessment in Infrastructure Networks
Leiden University and the Ministry of Infrastructure and Water Management are involved in a collaboration in the form of a research project titled 'Data-Driven Risk Assessment in Infrastructure Networks'.
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A document classifier for medicinal chemistry publications trained on the ChEMBL corpus
Source: J Cheminform, Volume 6, Issue 1 (2014)
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Tom KouwenhovenScience
t.kouwenhoven@liacs.leidenuniv.nl | +31 71 527 4799
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Surendra BalraadjsingScience
s.balraadjsing@cml.leidenuniv.nl | +31 71 527 2727
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Data-driven Predictive Maintenance and Time-Series Applications
Predictive maintenance (PdM) is a maintenance policy that uses the past, current, and prognosticated health condition of an asset to predict when timely maintenance should occur.
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Model-assisted robust optimization for continuous black-box problems
Uncertainty and noise are frequently-encountered obstacles in real-world applications of numerical optimization. The practice of optimization that deals with uncertainties and noise is commonly referred to as robust optimization.
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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…