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