Anthony Blaom

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](https://alan-turing-institute.github.io/MLJ.jl/dev/)
(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](https://github.com/ablaom/HelloJulia.jl/blob/dev/FIRST_STEPS.md) installed.

- **Strongly recommended,** Workshop resources pre-installed. See [here](https://github.com/ablaom/HelloJulia.jl/wiki/JuliaCon-2022-workshop:-Getting-started-with-Julia-and-MLJ).

- **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](https://github.com/ablaom/HelloJulia.jl)

## Format

This workshop will be a combination of formal presentation and live
coding.

Anthony Blaom

Talks:

Getting started with Julia and Machine Learning

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