Tutorials

Tutorials

Deep Learning:

  • Deep Learning by Ruslan Salakhutdinov at MLSS 2017
    Part 1: [Video] [Slides]
    Part 2: [Video] [Slides]
  • Deep Learning: Practice and Trends by Nando de Freitas, Scott Reed, Oriol Vinyals as NIPS 2017
    [Video] [Slides]

Bayesian Deep Learning:

  • Marrying Graphical Models & Deep Learning by Max Welling at MLSS 2017
    [Video] [Slides]

Bayesian Inference:

  • Bayesian Inference by Zoubin Ghahramani at MLSS 2017
    Part 1: [Video] [Slides]
    Part 2: [Video] [Slides]
    Part 3: [Video] [Slides]
  • Variational Inference: Foundations and Modern Methods by David Blei, Rajesh Ranganath, Shakir Mohamed at NIPS 2016
    [Video] [Slides]

Causality:

  • Learning Causal Mechanisms by Bernhard Schölkopf at ICLR 2018
    [Video]

Deep Generative Models:

  • Implicit Generative Models by Ilya Tolstikhin at MLSS 2017
    [Video] [Slides]
  • Generative Adversarial Networks by Ian Goodfellow at NIPS 2016
    [Video] [Slides]

Deep Gaussian Processes:

  • Deep Probabilistic Modelling with Gaussian Processes by Neil Lawrence at NIPS 2017
    [Video]

Reinforcement Learning:

  • Reinforcement Learning by Jan Peters at MLSS 2017
    [Video]
  • Deep Reinforcement Learning by Volodymyr Mnih at MLSS 2017
    Part 1: [Video]
    Part 2: [Video]
  • Deep Reinforcement Learning, Decision Making, and Control by Sergey Levine and Chelsea Finn at ICML 2017
    [Webpage] [Video] [Slides]