Leiden University logo.

nl en

Florence Nightingale Colloquium

Here you can find the recordings of previous Florence Nightingale Colloquia.

Florence Nightingale Colloquium 2022

Rob van der Mei, CWI and Vrije Universiteit Amsterdam

In life-threatening situations where every second counts, the timely presence of emergency services can make the difference the survival or death.  

In this talk, I will give a number of examples of success stories where data analytics, stochastics modeling and optimization have been successfully applied in real-life practice. In doing so, I will also address challenges involved in bringing academic research results to practice. 

Florence Nightingale Colloquium 2021

The Highs and Lows of Performance Evaluation: Towards a Measurement Theory for Machine Learning

This video can not be shown because you did not accept cookies.

You can leave our website to view this video.

Peter Flach, Professor of Artificial Intelligence at the University of Bristol

Abstract:
Our understanding of performance evaluation measures for machine-learned classifiers has improved considerably over the last decades. However, there is a range of areas where this understanding is still lacking, leading to ill-advised practices in classifier evaluation. This is clearly problematic, since if machine learning researchers are unclear about what exactly their experiments are telling them about their machine learning algorithms, then how can end-users trust systems deploying those algorithms?  

 

I suggest that in order to make further progress we need to develop a proper measurement theory of machine learning. Measurement theory studies the concepts of measurement and scale. If one has a way to measure, say, the length of individual rods or planks, this should also allow one to then calculate the combined length of concatenated rods or planks. What relevant concatenation operations are there in data science and AI, and what does that mean for the underlying measurement scale?

 

I discuss by example what such a measurement theory might look like and what kinds of new results it would entail. I furthermore argue that key properties such as classification ability and data set difficulty are unlikely to be directly observable, suggesting the need for latent-variable models. Ultimately, machine learning experiments need to go beyond simple correlations and aim to make causal inferences of the form 'Algorithm A outperformed algorithm B because the classes were highly imbalanced', or counterfactually, 'if the classes were re-balanced, this performance difference between A and B would not have been observed'. 

 

Short CV:
Peter Flach has been Professor of Artificial Intelligence at the University of Bristol since 2003. 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, and has an interest in human-centred AI. He is author of Simply Logical: Intelligent Reasoning by Example (John Wiley, 1994) and Machine Learning: the Art and Science of Algorithms that Make Sense of Data (Cambridge University Press, 2012).

 

Prof Flach stepped down last year as the Editor-in-Chief of the Machine Learning journal, after being in post for 10 years. He was Programme Co-Chair of the 1999 International Conference on Inductive Logic Programming, the 2001 European Conference on Machine Learning, the 2009 ACM Conference on Knowledge Discovery and Data Mining, and the 2012 European Conference on Machine Learning and Knowledge Discovery in Databases in Bristol. He is President of the European Association for Data Science, and a Fellow of the Alan Turing Institute for Data Science and Artificial Intelligence. 

TUD Motion Planning among Decision-Making Agents

This video can not be shown because you did not accept cookies.

You can leave our website to view this video.

Javier Alonso Mora, Associate Professor of Delft University of Technology

How to Manage Complexity in Healthcare: New Methods and Challenges for Health Analytics

This video can not be shown because you did not accept cookies.

You can leave our website to view this video.

Francesca Ieva, Senior Researcher Involved in Healthcare and Education Research

Optimizing Search and Recommender Systems based on Position-Biased User Interactions

This video can not be shown because you did not accept cookies.

You can leave our website to view this video.

Harrie Oosterhuis, assistant professor at the Data Science Group of the Institute of Computing and Information Sciences (iCIS) of Radboud University.

Bayesian Inversion for Tomography through Machine Learning

This video can not be shown because you did not accept cookies.

You can leave our website to view this video.

Ozan ├ľktem, Department of Mathematics, KTH - Royal Institute of Technology OF VAN UPPSALA

Competing Risks, Analysis and Interpretation

This video can not be shown because you did not accept cookies.

You can leave our website to view this video.

Ronals Geskus, Associate Professor at the University of Oxford

Learning How to Learn How to Learn

This video can not be shown because you did not accept cookies.

You can leave our website to view this video.

Joaquin Vanschoren, Assistant Professor of Machine Learning at the Eindhoven University of Technology (TU/e).

Making Deep Neural Networks Right for the Right Scientific Reasons

This video can not be shown because you did not accept cookies.

You can leave our website to view this video.

Kristian Kersting, Kristian Kersting is a Full Professor at the Computer Science Department of the TU Darmstadt University, Germany.

Florence Nightingale Colloquium 2020

Neural Augmentation with Applications in MRI Image Reconstruction and Wireless Communication

This video can not be shown because you did not accept cookies.

You can leave our website to view this video.

Max Welling,  Research Chair in Machine Learning at the University of Amsterdam and Distinguished Scientist at MSR.

Innovative Approaches on Parenting from a Family Perspective

This video can not be shown because you did not accept cookies.

You can leave our website to view this video.

Bernet Elzinga, Professor of Stress-related Psychopathology

Selecting Views in Multi-View Learning

This video can not be shown because you did not accept cookies.

You can leave our website to view this video.

Wouter van Loon, PhD candidate at the department of Methodology and Statistics and the Leiden Centre of Data Science (LCDS).

Imaging Brain Networks: Pharmacological Manipulation and Individual Prediction of Cognitive Decline

This video can not be shown because you did not accept cookies.

You can leave our website to view this video.

Serge Rombouts, Professor of Methods of Cognitive Neuroimaging

This website uses cookies.