Machine Learning with Telemetry Data

Overview

Teaching: 0 min
Exercises: 0 min

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Objectives

This page archives the lesson materials for “Machine Learning with Telemetry Data”, given by Jake Brownscombe at the 2024 OTN Symposium ECR Workshop.

Abstract

Machine learning approaches are highly useful for characterizing the underlying structure of complex telemetry datasets and developing accurate predictive models of ecological systems. This workshop will cover a range of basic to more advanced machine learning techniques with acoustic telemetry data, with the aim of providing useful knowledge and analytical code for those with varied experience levels in this field. This is a re-occurring workshop that has been presented previously at OTN and GLATOS meetings, with new developments and contributions with each iteration.

This workshop covers applications of machine learning techniques to acoustic telemetry data, from the basics to some more advanced techniques. Students and researchers of all levels may find value here, in exploring the underlying structure of their telemetry data and developing accurate predictive models of ecological systems.

Code

The code and data are available at this repository. Instructions for accessing the GitHub repository are on the setup page.

Key Points