Machine Learning

Fundamentals of Machine Learning: a BSc course that covers the basics of machine learning, including linear classification and regression, support vector machines, random forests, and neural networks.

This poster recaps two collaboration projects I did during my time as Niels Stensen Fellow at the University of Copenhagen. The main …

Sequential Domain Adaptive Intelligent Systems focuses on domain-adaptative classification over an ordered sequence of biased samples. An example of such a sequence is medical data from hospital along a geographic path.

One can often not evaluate a classifier in the target domain due to the absence of target labels. Fortunately, in the covariate shift …

NVPHBV is the Dutch Society for Pattern Recognition and Image Processing. During their meetings, researchers from the Netherlands have …

Importance-weighting is a popular and well-researched technique for dealing with sample selection bias and covariate shift. It has …

SNN organized a one day symposium entitled Intelligent Machines, where an overview of recent developments was presented. The meeting aimed to establish a dialogue and to build connections between academic research, industry and public institutions in the Netherlands. I presented my preliminary work on incorporating transfer models in domain-adaptive classifiers.

Domain Adaptive Pattern Recognition explores the limits of generalization for a special case of statistical learning where training data and test data are differently biased samples of some underlying data-generating distribution.