Lectures and Tutorials
Follow the instructions on the GitHub repository on how to do the setup for the tutorials/labs.
Deep Learning (Kevin Webster, Pierre Richemond, Kai Arulkumaran)
Optimization (John Duchi)
Variational Inference (Shakir Mohamed)
Reinforcement Learning (Katja Hofmann)
Interpretability (Sanmi Koyejo)
Gaussian Processes (James Hensman)
Kernels (Lorenzo Rosasco)
MCMC (Michael Betancourt)
AI for Good (Julien Cornebise and Moustapha Cisse)
Approximate Bayesian Computation (Sarah Filippi)
Fairness and Ethics in AI (Timnit Gebru)
Speech Processing (Karen Livescu)
Learning Theory (Samory Kpotufe)
Machine Learning in Computational Biology (Barbara Engelhardt)
Submodularity (Stefanie Jegelka)