Abstract: The resurging interest in machine learning is due to multiple factors including growing volumes and varieties of data,and cheaper computational processing. Thus making it possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results on a very large scale.Scikit-learn (http://scikit-learn.org/) has emerged as one of the most popular open source machine learning toolkits, now widely used in academia and industry. scikit-learn provides easy-to-use interfaces in Python to perform advanced analysis and build powerful predictive models.
Bio: Andreas Mueller received his MS degree in Mathematics (Dipl.-Math.) in 2008 from the Department of Mathematics at the University of Bonn. In 2013, he finalized his PhD thesis at the Institute for Computer Science at the University of Bonn. After working as a machine learning scientist at the Amazon Development Center Germany in Berlin for a year, he joined the Center for Data Science at the New York University in the end of 2014. In his current position as assistant research engineer at the Center for Data Science, he works on open source tools for machine learning and data science. He is one of the core contributors of scikit-learn, a machine learning toolkit widely used in industry and academia, for several years, and has authored and contributed to a number of open source projects related to machine learning.
Lecturer at Columbia University