JuliaCon 2022 (Times are UTC)

Getting started with Julia and Machine Learning
07-20, 18:00–21:00 (UTC), Green

A three-hour introductory workshop for newcomers to Julia and machine
learning. Participants will have training in some technical domain,
for example, in science, economics or engineering. While no prior
experience with Julia or machine learning is needed, it is assumed
participants have Julia 1.7 installed on their computer.


In their simplest manifestation, machine learning algorithms extract,
or "learn", from historical data some essential properties enabling
them to respond intelligently to new data (typically,
automatically). For example, spam filters predict whether to designate
a new email as "junk", based on how a user previously designated a
large number of previous messages. A property valuation site suggests
the sale price for a new home, given its location and other
attributes, based on a database of previous sales.

Julia is uniquely positioned to accelerate developments in machine
learning and there has been an explosion of Julia machine learning
libraries. MLJ
(Machine Learning in Julia) is a popular toolbox providing a common
interface for interacting with over 180 machine learning models
written in Julia and other languages. This workshop will introduce
basic machine learning concepts, and walk participants through enough
Julia to get started using MLJ.


  • Essential. A computer with Julia 1.7.3 installed.

  • Strongly recommended, Workshop resources pre-installed. See here.

  • Recommended. Basic linear algebra and statistics, such
    as covered in first year university courses.

  • Recommended but not essential. Prior experience with a scripting
    language, such as python, MATLAB or R.


  • Be able to carry out basic mathematical operations using Julia,
    perform random sampling, define and apply functions, carry out
    iterative tasks

  • Be able to load data sets and do basic plotting

  • Understand what supervised learning models are, and how to evaluate
    them using a holdout test set or using cross-validation

  • Be able to train and evaluate a supervised learning model using
    the MLJ package




This workshop will be a combination of formal presentation and live

Anthony Blaom Anthony Blaom is a mathematician, publishing in areas of pure mathematics, and a scientific computing consultant. He is a co-creator and lead contributor for MLJ, an open-source machine learning platform written in Julia.

Dr. Blaom was initially trained as a mechanical engineer at the University of Melbourne in 1991. After completing a MSc in Aeronautics and a PhD in Mathematics at Caltech in 1998, he joined the University of Auckland as a Lecturer. For a while he switched to adjunct teaching, focusing on his young children, whom he homeschooled while living on the small island of Waiheke.

Dr. Blaom is currently a Senior Research Fellow in the Department of Computer Science, University of Auckland.