Machine learning promises to revolutionise life science research by speeding up data analysis, enabling prediction of biological patterns and modelling complex biological systems.

But what exactly is machine learning and when should you use it?

This hands-on online workshop provides a high-level introduction to machine learning: what it is, its advantages and disadvantages compared to traditional modelling approaches and the types of scenarios where it may be the right tool for the job. 

Using example datasets and basic machine learning pipelines we contrast a few commonly used algorithms for constructing predictive models and explore some of their trade-offs. We discuss common pitfalls in how machine learning is applied and evaluated, with a focus on its application in the life sciences, to help you recognise overly optimistic results. We discuss how and why such errors arise and strategies to avoid them. 

Lead Trainer: Dr Benjamin Goudey, Research Fellow, Florey Department of Neuroscience and Mental Health

Date/Time: 11 June 2024, 11am - 3pm AEST / 10:30am - 2:30pm ACST / 9am - 1pm AWST  (check in your time zone)

Location: Online

Format:

In this online workshop expert trainers introduce new topics and guide you through hands-on activities to help you put your new skills into action. The hands-on exercises make use of a Google Colab notebook in which you can adapt and run provided code.

Learning outcomes:

By the end of the workshop you should be able to:

• Give a high-level description of what machine learning is and what it can do

• Explain the basics of evaluating supervised machine learning models

• Recognise when evaluation of machine learning models is optimistically biased

• Outline types of models and metrics

• Explore and extend some R code for implementing machine learning pipelines

What you will not learn:

• Detailed knowledge of algorithms underpinning machine learning models

• Anything that is not supervised (reinforcement learning, unsupervised learning)

• How to run the latest and greatest deep-learning/AI models

• Details around data cleaning, engineering, organisation

Who the workshop is for:

This workshop is for Australian researchers who want to know more about machine learning and who are considering using it as part of their projects. You must be associated with an Australian organisation for your application to be considered.

Prerequisites

This workshop assumes some familiarity with R. You do not need to be an expert but you should be able to set up directories, run commands, read in and output files and be familiar with the “tidyverse” collection of packages.

Code will be provided in a Google Colab Notebook. The expectation is that you follow along rather than write this code from scratch. 

How to apply:

Applications have closed

This workshop is free but participation is subject to application with selection. 

Applications close at 11:59pm AEST, Friday 24 May 2024.

Applications will be reviewed by the organising committee and all applicants will be informed of the status of their application (successful, waiting list, unsuccessful). Successful applicants will be provided with a Zoom meeting link closer to the date. More information on the selection process is provided in our Advice on applying for Australian BioCommons workshops.

This workshop is presented by the Australian BioCommons, with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative.

This event is part of a series of bioinformatics training events. If you'd like to hear when registrations open for other events, please subscribe to Australian BioCommons

Cover image: Adobe Stock 432173706