CPSC 532W 101 2022W

Instructor(s)
Frank Wood
Course Page
https://www.cs.ubc.ca/~fwood/CS532W-539W/
Extended Description

Introduction

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
Section
101
Term
Term 1
Session
2022W
Dates
Days
Mon Wed
Time (start)
11:00 AM
Time (end)
12:30 PM
Date (start)
Date (end)