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SUMMARY:Data-driven versus Hypothesis-driven approaches in cognitive neuro
 science - Karim Jerbi\, PhD\, Canada Research Chair (CRC) University of Mo
 ntreal
DTSTART:20210528T151500Z
DTEND:20210528T170000Z
UID:TALK160339@talks.cam.ac.uk
CONTACT:Matthew Attwaters
DESCRIPTION:The spectacular success of artificial intelligence in recent y
 ears has triggered a surge in the use of data-driven methods in many field
 s of research. In cognitive neuroscience\, brain decoding and encoding par
 adigms based on machine learning have recently attracted a lot of attentio
 n. Yet\, amid strong claims about the endless power of AI and how it is go
 ing to revolutionize the way we do research\, legitimate questions arise: 
 Is the age of good-old hypothesis-driven neuroscience coming to an end? An
 d what is the added value\, if any\, of machine-learning powered data-driv
 en brain research? In this talk\, I will begin with an introduction to the
  basics of machine learning and then provide illustrative examples of how 
 my lab combines electrophysiological brain measurements\, spectral analyse
 s and machine learning to probe the link between oscillatory brain dynamic
 s and various cognitive processes. I also promise to show a lot of pandas 
 on my slides.\n
LOCATION:Zoom meeting
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