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DTSTAMP:20260707T193342Z
UID:4be6d962-f3ee-4d25-9845-2308f9305087
DTSTART:20250622T230000Z
DTEND:20250623T070000Z
DESCRIPTION:Machine learning promises to revolutionise life science researc
 h by speeding up data analysis\, enabling prediction of biological pattern
 s and modelling complex biological systems.\n\nBut what exactly is machine
  learning and when should you use it?\n\nThis hands-on online workshop pro
 vides a high-level introduction to machine learning: what it is\, its adva
 ntages and disadvantages compared to traditional modelling approaches and 
 the types of scenarios where it may be the right tool for the job. \n\nUsi
 ng example datasets and basic machine learning pipelines we contrast a few
  commonly used algorithms for constructing predictive models and explore s
 ome of their trade-offs. We discuss common pitfalls in how machine learnin
 g is applied and evaluated\, with a focus on its application in the life s
 ciences\, to help you recognise overly optimistic results. We discuss how 
 and why such errors arise and strategies to avoid them. \n\n**Lead Trainer
 :** Dr Benjamin Goudey\, AI Technical Lead\, Australian BioCommons\n\n**Da
 te/Time:** 19 - 20 August 2025\, 1pm - 4pm AEST/12:30pm - 3:30pm ACST/11am
  - 2pm AWST (Check in your timezone)\n\n**Location:** Online\n\n**Format:*
 *\n\nThis online workshop takes place over two sessions. Expert trainers w
 ill introduce new topics and guide you through hands-on activities to help
  you explore these concepts. The hands-on exercises make use of a Google C
 olab notebook in which you can adapt and run provided code.\n\n**Learning 
 outcomes:**\n\nBy the end of the workshop you should be able to:\n\n* Give
  a high-level description of what machine learning is and what it can do\n
 \n* Explain the basics of evaluating supervised machine learning models\n\
 n* Recognise when evaluation of machine learning models is optimistically 
 biased\n\n* Outline types of models and metrics\n\n* Explore and extend so
 me R code for implementing machine learning pipelines\n\nWhat you will not
  learn:\n\n* Detailed knowledge of algorithms underpinning machine learnin
 g models\n\n* Anything that is not supervised (reinforcement learning\, un
 supervised learning)\n\n* How to run the latest and greatest deep-learning
 /AI models\n\n* Details around data cleaning\, engineering\, organisation\
 n\n**Who the workshop is for:**\n\nThis workshop is for Australian researc
 hers 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 Austral
 ian organisation for your application to be considered.\n\n**Prerequisites
 **\n\nSome familiarity with R is recommended. You do not need to be an exp
 ert but you should be able to set up directories\, run commands\, read in 
 and output files and be familiar with the “tidyverse” collection of pa
 ckages.\n\nCode will be provided in a Google Colab Notebook. The expectati
 on is that you follow along rather than write this code from scratch. \n\n
 **How to apply:**\n\n[Apply here](https://www.eventbrite.com.au/e/workshop
 -machine-learning-in-the-life-sciences-tickets-1303775009149?aff=oddtdtcre
 ator)\n\nThis workshop is free but participation is subject to application
  with selection. \n\nApplications close at 11:59pm AEST\, Friday 1 August 
 2025.\n\nApplications will be reviewed by the organising committee and all
  applicants will be informed of the status of their application (successfu
 l\, waiting list\, unsuccessful). Successful applicants will be provided w
 ith a Zoom meeting link closer to the date. More information on the select
 ion process is provided in our Advice on applying for Australian BioCommon
 s workshops.
SUMMARY:WORKSHOP: Machine learning in the life sciences
URL;VALUE=URI:https://www.biocommons.org.au/events/machine-learning-wkshp-2
 025
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