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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Self Paced

Learn the principles of machine learning and the importance of algorithms. Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications. This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. We will also examine why algorithms play an essential role in Big Data analysis.

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