Machine and Deep Learning

(Open Group)

There are now numerous cost-effective ways to collect biological data at large spatial scales and long survey windows, thereby increasing the statistical power of conservation monitoring efforts. To be effective at a global scale, these new tools must be coupled with automated approaches to analyzing data streams. Here we focus on details of how advances in machine and deep learning can be used  to extract meaningful information from the torrent of new sensor data, from satellite data down to eDNA data, and thus improve the adaptive management of natural systems. 

David Klein is the manager of this group, get in touch through his profile if you have ideas or questions about this community group.