Lecture slides
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Using normative, shallow and deep neural networks to identifying the neural architecture of human perceptualdecision making Jean-Rémi King, Psychology Department, NYU
Probabilistic Machine Learning for Lesion and Tumour Detection, Segmentation and Disease Prediction inPatient Brain Images [Keynote lecture]
Tal Arbel, Centre for Intelligent Machines, McGill University
What we can learn and predict when we model the brain as a graph? Jonas Richiardi, Dept Radiology, Lausanne University Hospital & Advanced Clinical Imaging Technology, Siemens Healthineers, Switzerland
Computational Psychiatry [Keynote lecture] Michael J. Frank, Computation in Brain and Mind, Brown University
Deep Learning 101 Pascal Vincent, MILA, Université de Montréal
Machine learning models for temporal prediction and decision making in the brain Doina Precup, MILA, McGill University
Neural Coding with deep learning Marcel van Gerven, Radbound University
Mapping the brain, its function and populations: a deep learning approach [Keynote lecture] Sergey Plis, Machine Learning in Neuroscience Lab, University of New Mexico
Adaptive treatment of epilepsy via reinforcement learning Joelle Pineau, Computer Science Department, McGill University
The bumpy road to strong AI Paul Cisek, Neuroscience, Université de Montréal
Towards an integration of deep learning and neuroscience Adam Marblestone, MIT Media Lab., Harvard University
Bridging the gap between brains, cognition and deep learning [Keynote lecture] Yoshua Bengio, MILA, Université de Montréal