报告人：Professor Tom Heskes
报告题目：Bayesian machine learning
摘要简介：Machine learning is about learning models from data. In so-called Bayesian machine learning we build probabilistic models and use probability calculus, in particular Bayes' rule, to infer the unknown model parameters given the observed data. In my presentation I will show where this leads to by highlighting some of the applications that we work on: brain-computer interfacing (how to control devices by reading out brain activity), functional genomics (how to use functional and structural data to unravel the life cycle of the malaria parasite), and personalization of hearing aids (how to design listening experiments that reveal the preferences of individual users).
报告人简介：Dr Tom Heskes is a Professor in Artificial Intelligence, and he leads the Machine Learning Group, at the Institute for Computing and Information Sciences, Radboud University Nijmegen, the Netherlands. He is further affiliated Principal Investigator at the Donders Centre for Neuroscience.
Prof Heskes' research is on artificial intelligence, in particular (Bayesian) machine learning. He works on Bayesian inference (approximate inference, hierarchical modeling, dynamic Bayesian networks, preference elicitation); machine learning (multi-task learning, bias-variance decompositions); and neural networks (on-line learning, self-organizing maps, time-series prediction). In a nutshell, he and the members of his group use probability calculus and statistics to design and understand "intelligent" systems that can learn from data. He is also involved in several projects that concern applications in, among others, brain-computer interfaces, adaptive personalization of hearing aids, and bioinformatics. Prof Heskes has published over 100 research papers and books in the above area.