Dr. Hu to lead an AAG Workshop on Integrating Machine Learning into Geographic Research

With the many challenges posed by the pandemic of COVID-19, the American Association of Geographers (AAG) called upon geography faculty members throughout the nation to help offer a series of virtual workshops and seminars (for AAG members only) to support graduate students adapt in their research. Dr. Hu is one of these selected faculty members.

Our workshop is “Integrating Machine Learning into Geographic Research”. It will introduce students to the fundamental concepts and techniques related to machine learning, and how to integrate machine learning into geographic research. This workshop is designed to help students overcome some of the challenges posted by the pandemic by leveraging a free cloud computing platform, Google Colab, that allows students to build machine learning models on super computers safely at home and for free. The main programming language for this workshop will be Python, and the main machine learning package will be scikit-learn. Students can find more details or register here: https://www.eventbrite.com/e/132423234459

Workshop schedule

The workshop will start on Feb 8 and end on Feb 12 (note: the interactive sessions will not be recorded.)

  • Monday, asynchronous: Intro to GeoAI and Google Colab: A pre-recorded lecture consisting of two videos is made available. We will give a brief introduction to geospatial artificial intelligence (GeoAI) and help students set up the working environment on Google Colab.
  • Tuesday, synchronous, 9:00 – 11:00 AM (ET): Python Refresh and GeoPandas: We will have a 2-hour live session on Zoom. This session will help you refresh Python programming basics and learn how to work with shapefile data using the GeoPandas library.
  • Wednesday, synchronous, 9:00 – 11:00 AM (ET): Machine learning for Geography: We will have a 2-hour live session on Zoom. This session will cover the basics of preparing geographic data for machine learning models and implementing a model (Random Forest) using the scikit-learning library.
  • Thursday, asynchronous: Exercise: Building Your Own Machine Learning Model A simple exercise will be released at the end of the session on Wednesday. Students are expected to work on this exercise on Thursday, in which you will be asked to build your own model using scikit-learning.
  • Friday, synchronous, 9:00 – 11:00 AM (ET): Recap and the Road Forward: We will have a 2-hour live session on Zoom. In this session, I will review the exercise that you have worked on Thursday, and answer any questions. I will also share some resources for studying machine learning and AI beyond this workshop.

This workshop series is supported by AAG staff Coline Dony and Julaiti Nilupaer. Our GeoAI Lab thanks their great effort and dedication to help our graduate students go through this challenging time!