In this module you will progress your knowledge of different environmental data sources, including
sensors and sensor systems by linking the practical aspects of detecting and monitoring wildlife
populations from the 'Technology for Nature' with the advanced analytical skills required to interpret, understand, and infer patterns, processes, behaviour, and trends from collected data. You will build
upon the theoretical exploration of how, what, where, and when to monitor wildlife from Term 1 by
applying appropriate programming and statistical methods in R and Python using the open-source
Anaconda environment. This will be facilitated by a suite of lectures, in-depth hands-on tutorials, and
e-learning, within which you will learn how to critically evaluate training data, model outcomes and
predictions, and understand the importance of linking chosen approaches with key concepts in
ecological theory in order to design appropriate survey and monitoring systems. Key subject areas
will include:
Big data and biodiversity
AI for beginners
Unsupervised and supervised machine learning
Clustering analyses
Applied machine learning approaches to DNA analysis
Visual and acoustic data processing and analyses
Occupancy and abundance modelling
Data and ethics

EARLY MODERN HISTORY|EUROPEAN HISTORY|INTELLECTUAL HISTORY|FRENCH REVOLUTION

This module is for half-year affiliate students only. Please see the 30-credit full-year version, HIST0674, for the full module description.

Please be aware...

Enlightenments and Revolutions Affiliate (HIST0533)

This module is for half-year affiliate students only. Please see the 30-credit full-year version, HIST0674, for the full module description.

Please be aware that you will only be covering half of the module content described in the full module description (content covered in Term 1 or Term 2, depending on your time with us).

If you require any further information, please contact the History Taught Programmes Team.

This module is for students who have successfully completed Level 1 of the target language, or who can demonstrate equivalent competence. The module consists...