Title: "Machine Learning in Python with scikit-learn"
Date: Friday 2/13
Time: 11am
Room: CUNY Graduate Center Room 6496

scikit-learn 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 to perform advances analysis and build powerful predictive models.
The tutorial will cover basic concepts of machine learning, such as supervised and unsupervised learning,
cross validation and model selection. We will see how to prepare data for machine learning, and go from applying
a single algorithm to building a machine learning pipeline.

Andreas Mueller finalized his PhD thesis on structured prediction for image segmentation
at the Institute for Computer Science at the University of Bonn in 2013.
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 position as assistant research
engineer at the Center for Data Science, he works on open source tools for
machine learning and data science.
He has been one of the core contributors and maintainer of scikit-learn, a
machine learning toolkit widely used in industry and academia, for several
years, and authored and contributed to a number of open source projects related
to machine learning.

A brief note from Andreas: 
I usually recommend that people install the anaconda python distribution before coming: 

and make sure that they are able to run the ipython notebook. 
If they want to follow along, there will be material online that 
they can download beforehand, either here: