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Paper: Applications of machine learning in animal behaviour studies

A colleague pointed out this new review paper in the journal "Animal Behaviour": Applications of machine learning in animal behaviour studies.

It's a useful introduction to machine learning for animal behaviour people. In particular, the distinction between machine learning (ML) and classical statistical modelling is nicely described (sometimes tricky to convey that without insulting one or other paradigm).

The use of illustrative case studies is good. Most introductions to machine learning base themselves around standard examples predicting "unstructured" outcomes such as house prices (i.e. predict a number) or image categories (i.e. predict a discrete label). Two of the three case studies (all of which are by the authors themselves) similarly are about predicting categorical labels, but couched in useful biological context. It was good to see the case study relating to social networks and jackdaws. Not only because it relates to my own recent work with colleagues (specifically: this on communication networks in songbirds and this on monitoring the daily activities of jackdaws - although in our case we're using audio as the data source), but also because it shows an example of using machine learning to help elucidate structured information about animal behaviour rather than just labels.

The paper is sometimes mathematically imprecise: it's incorrect that Gaussian mixture models "lack a global optimum solution", for example (it's just that the global optimum can be hard to find). But the biggest omission, given that the paper was written so recently, is any real mention of deep learning. Deep learning has been showing its strengths for years now, and is not yet widely used in animal behaviour but certainly will be in years to come; researchers reading a review of "machine learning" should really come away with at least a sense of what deep learning is, and how it sits alongside other methods such as random forests. I encourage animal behaviour researchers to look at the very readable overview by LeCun et al in Nature.

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