PGM Group

Machine Learning Training and Tutorials

It is mainly to provide training for my students, but it is open to everyone who is interested. I hope this can propagate knowledge, and inspire ideas and foster innovation. I will be presenting a bulk of the program, and will try to make it valuable to not only students, but also postdocs and academics. Note that not all content of the talks are written in the slides. Roughly 40% of the content were expressed on white board or verbally.
  1. Title: What is machine learning? From the shallow end to deep graph neural networks [ pdf], 22 Nov., 2018. Speaker: Javen Shi.

    Abstract: I will be covering the basics of machine learning. I will explain the concepts, theory, applications, and industry's expectation. I will then move from traditional machine learning to deep learning. In particular, I will focus on DeepMind's latest work, deep graph neural networks (https://arxiv.org/abs/1806.01261), which can recover many very recent methods in the intersection of graphical models and deep learning. I will also share my thoughts on the challenges and opportunities.

  2. Title: Deep graph networks and Support Vector Machines [ pdf1 pdf2], 29 Nov., 2018. Speaker: Javen Shi.

    Abstract: I will continue to cover DeepMind's graph networks (https://arxiv.org/abs/1806.01261), and fill in some background of graphical models. I will also cover Support Vector Machines and its related background such as convexity and optimisation.

  3. Title: Support Vector Machines [ pdf], 6 Dec., 2018. Speaker: Javen Shi.

    Abstract: I will continue to cover Support Vector Machines (SVM), including binary class SVM, one class SVM (for novelty detection), and briefly mention multi-class SVM and structured SVM.

  4. Title: Generative Adversarial Networks (GANs) and Beyond, 13 Dec., 2018. Speaker: Ehsan Abbasnejad.

    Abstract: Ehsan will cover Generative Adversarial Networks (GANs) and other deep generative models such as variational autoencoders (VAE).

University Courses

Past Tutorials

Probabilistic Graphical Models

  1. Representation [ pdf], ACVT, UoA, April 15, 2011

  2. Inference [ pdf], ACVT, UoA, May 6, 2011

  3. Learning [ pdf], ACVT, UoA, May 27, 2011

  4. Sampling-based approximate inference [ pdf], ACVT, UoA, June 10, 2011

  5. Temporal models [ pdf], ACVT, UoA, August 12, 2011

Generalisation Bounds

  1. Basics [ pdf], ACVT, UoA, April 13, 2012

  2. VC dimensions and bounds [ pdf], ACVT, UoA, April 27, 2012

  3. Rademacher complexity and bounds [ pdf], ACVT, UoA, August 17, 2012

  4. PAC Bayesian Bounds, [ pdf], ACVT, UoA, August 31, 2012

  5. Regret bounds for online learning, [ pdf], ACVT, UoA, Nov. 2, 2012

Please email me if you find errors or typos in the slides.