May 30, 2014

LAB: Machine Learning Applications & Project-Specific Solutions for NYC*

In this follow-up lab session we will introduce and practice some core machine learning algorithms on real-world open urban data. We will go through the full analysis cycle from data processing, feature extraction, model building, prediction and validation and discuss the major algorithms and their characteristics in terms of scalability and problem suitability. We will then discuss specific applications of machine learning for city agencies based on the audience needs and goals, identify tools and publically available resources across programming languages and environments, and go in technical depth in some of the key tools and algorithms.

This is an advance session, open to participants with computer programming knowledge.

Participation Requirements:

  • Attendance to May 19th lecture on Machine learning
  • Good knowledge of a scripting language such as Python/R/Matlab.
  • Some knowledge of linear algebra/statistics

Participants will receive a certificate of attendance from New York University’s Center for Urban Science and Progress (NYU CUSP)