Peter Orbanz

Before coming to New York, he was a Research Fellow in the Machine Learning Group of Zoubin Ghahramani at the University of Cambridge, and previously a graduate student of Joachim M. Buhmann at ETH Zurich.
His main research interests are the statistics of discrete objects and structures: permutations, graphs, partitions, and binary sequences. Most of his recent work concerns representation problems and latent variable algorithms in Bayesian nonparametrics. More generally, he is interested in all mathematical aspects of machine learning and artificial intelligence.

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Machine Learning for Data Science and Analytics (edX) EdX
Columbia University,ColumbiaX

Machine Learning for Data Science and Analytics (edX)

Dive into the fascinating world of Machine Learning with our edX course, tailored for both novices and experienced professionals seeking to enhance their Data Science and Analytics capabilities. This course unravels the principles behind machine learning algorithms and their pivotal role in various applications such as web search, ad placement, credit scoring, stock trading, and more.

Self Paced
Self-Paced
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