This workshop will provide an introductory overview of supervised machine learning as a general approach to building classification and regression models from a set of example observations. We will begin with an overview of the topic, highlighting a few common algorithms. We will subsequently dive into some hands-on examples using the Python programming language and the Jupyter Notebook, a web application widely used for developing and sharing code, data visualizations, and analyses.
Previous programming experience is not required. We recommend having a fully charged device with Jupyter and Python 3 already installed for the workshop. Virtual desktops will be available for those who cannot download the software.
This introductory workshop is presented in collaboration with UMaine’s Advanced Computing Group.
To attend, please register online in advance.
About the Presenter
Kasey Legaard is a Research Assistant Professor in the Center for Research on Sustainable Forests and the School of Forest Resources at the University of Maine. His research revolves around the development and application of supervised machine learning methods in satellite remote sensing.