Contact FutureLearn for Support
Skip main navigation
We use cookies to give you a better experience, if that’s ok you can close this message and carry on browsing. For more info read our cookies policy.
We use cookies to give you a better experience. Carry on browsing if you're happy with this, or read our cookies policy for more information.

Skip to 0 minutes and 9 secondsWelcome to week two of this MOOC. This week, we'll focus on machine learning. We will explain two scientific studies that used machine learning. We also see how volunteered geographic information can be used in scientific work. From an analytical perspective, we will first see a study relating tick occurrence to environmental variables, and our second example is a diffusion study comparing the spatial temporal diffusion patterns of outbreaks of measles. Although the topics both use machine learning, they can be taken independently. We hope you will enjoy these topics.

Week 2 - machine learning

Welcome back to our course in GeoHealth. We hope that you have learned some new things in the past week.

This week, you will learn more on machine learning, a group of techniques that may become very useful when datasets are increasing in volume. The week contains two in-depth topics.

The first in-depth topic is presented by Raul Zurita-Milla and Irene Garcia Marti from the University of Twente. This topic introduces a unique study in which information on tick bites is collected.

The second in-depth topic is presented by Ellen-Wien Augustijn from the university of Twente. In this topic you will find out more on how diffusion patterns of Measles can be captured using Self-Organizing Maps.

We hope you will enjoy both topics.

Share this video:

This video is from the free online course:

Geohealth: Improving Public Health through Geographic Information

University of Twente

Course highlights Get a taste of this course before you join: