CPSC 532W 101 2022W

Extended Description


Probabilistic programming lies at the intersection of machine learning, statistics, programming languages, and deep learning. Historically probabilistic programming has been about automating Bayesian statistical inference; more recently it has emerged as a candidate for the next toolchain for AI, particularly for unsupervised, semi-supervised, and reinforcement learning.

Learning Outcomes

By the end of this course you will know how to (if you don’t already):

  • Write a general-purpose inference engine for graphical models specified via a probabilistic programming language
  • Write a general-purpose inference engine for higher-order probabilistic programming language

Skills and knowledge that you will reinforce or acquire:

  • What a model is
  • How to use probabilistic programming systems to solve inference problems automatically
  • The relationship between generative models, stochastic simulators, and decoders
  • What inference is, various algorithms for performing inference, and what their characteristics are

You will also be exposed to a variety of “advanced” models and methods including program synthesis via inference, deep structured variational autoencoders for semi- and un-supervised learning as well as a raft of advanced inference methods including Hamiltonian Monte Carlo, sequential Monte Carlo and stochastic variational inference.

Course Info
Term 1
Mon Wed
Time (start)
11:00 AM
Time (end)
12:30 PM
Date (start)
Date (end)