In the Machine Learning Tutorial Series, external guest speakers will give tutorial lectures on focused machine learning topics. The target audience are undergraduates, MSc and PhD students, post-docs and interested faculty members.
All talks will be announced via the ml-talks mailing list.
If you are looking for previous tutorials, check out the ML Tutorials Archive.
Normally, the talks will be on Wednesdays, 14:00 – 16:00.
|2018-01-31||Joanna Bryson (University of Bath)||
|2018-03-28||Emma Robinson (King’s College London)||The Symbiotic Relationship Between Neuroscience and Machine Learning|
The Symbiotic Relationship Between Neuroscience and Machine Learning (Emma Robinson, 2018-03-28)
The quest to generate effective modern machine learning and artificial intelligence systems has frequently turned to models of human brain function for inspiration. In this tutorial I will discuss the way in which understanding of the human memory and visual systems have moulded the design of certain artificial intelligence systems, before talking at length about the role that machine learning, and now more recently Deep Learning, is playing in the pursuit to better understand higher-order brain function. Enhanced knowledge of advanced human cognitive systems offers significant potential for improved diagnosis and treatment of complex neurological diseases, as well as potentially inspiring the next generation of AI. By the end of this tutorial the audience should come away with a solid practical guide of the constraints that frame how advanced machine learning techniques should be applied to neuroimaging data sets. I will finish with a discussion of the ways in which development of new machine learning methodology might enhance neuroimaging in future.