Fogler Library Faculty Newsletter 11-17-2020

Literature Review Challenge Now Online, Supervised Machine Learning Workshop

  1. Literature Review Challenge Now Online
  2. Supervised Machine Learning Workshop

Featured Resource: Statista

Statista integrates data on over 80,000 topics from over 18,000 sources onto a single platform. Categorized into 21 market sectors, provides direct access to quantitative data on media, business, finance, politics, and a wide variety of other areas of interest or markets. Data sources include market research reports, such as the Ipsos Affluent Survey published annually by Ipsos Media, Simmons National Consumer Studies and Consumer Insights from Scarborough Research, as well as trade publications, scientific journals, and government databases. Statista is also a source for infographics on a variety of topics.

1. Literature Review Challenge Now Online

If you missed last week’s Literature Review Challenge, you can still participate! The Literature Review Challenge is now available online. The Challenge provides a series of brief tasks designed to help you make the most of your literature searching, including how to strategically search for literature in the library, Google Scholar, and beyond, how to stay on top of literature in your field, approaches to organizing and thematizing your literature, and how to avoid link rot in your reference list.

2. Supervised Machine Learning Workshop
November 18 @ 6:00 pm – 7:00 pm

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.