The Columbia Year of Statistical Machine Learning aims to bring together leading researchers whose work is at the forefront of theoretical, methodological, and applied statistical machine learning.  At a high level, statistical machine learning aims to develop statistical methodology that allows computers to extrapolate from complex observations. Such methods have proved to be powerful tools in many computer science and engineering applications like image processing, artificial intelligence, and natural language processing, as well as in other disciplines like astronomy, genetics, finance, physics, and medicine.

The Columbia Year of Statistical Machine Learning will consist of bi-weekly seminars, workshops, and tutorial-style lectures, with invited speakers.

These activities are made possible through the generous support of the Department of Statistics and the TRIPODS Institute at Columbia University.

Organizing committee: Daniel Hsu, Samory Kpotufe, Arian Maleki, Cindy Rush.