Getting started with Julia and Machine Learning

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.

Overview

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.

Prerequisites

• 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.

Objectives

• 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

Resources

HelloJulia.jl

Format

This workshop will be a combination of formal presentation and live coding.