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BEGIN:VEVENT
DTSTAMP:20260615T015925Z
UID:8acb5c42-f55a-4d9f-ae7e-8c1c01626159
DTSTART:20240611T010000Z
DTEND:20240611T050000Z
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\, Research Fellow\, Florey Department of Neuroscien
 ce and Mental Health\n\n**Date/Time:** 11 June 2024\, 11am - 3pm AEST/10:3
 0am - 2:30pm ACST/9am - 1pm AWST \n\n**Location:** Online\n\n**Format:**\n
 \nIn this online workshop expert trainers introduce new topics and guide y
 ou 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.\n\n**Learning outcomes:**\n\nBy the end o
 f the workshop you should be able to:\n\n* Give a high-level description o
 f what machine learning is and what it can do\n* Explain the basics of eva
 luating supervised machine learning models\n* Recognise when evaluation of
  machine learning models is optimistically biased\n* Outline types of mode
 ls and metrics\n* Explore and extend some R code for implementing machine 
 learning pipelines\n\nWhat you will  **not ** learn:\n\n* Detailed knowled
 ge of algorithms underpinning machine learning models\n* Anything that is 
 not supervised (reinforcement learning\, unsupervised learning)\n* How to 
 run the latest and greatest deep-learning/AI models\n* Details around data
  cleaning\, engineering\, organisation\n\n**Who the workshop is for:**\n\n
 This workshop is for Australian researchers who want to know more about ma
 chine learning and who are considering using it as part of their projects.
  You must be associated with an Australian organisation for your applicati
 on to be considered.\n\n_Prerequisites_\n\nThis workshop assumes some fami
 liarity 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 famili
 ar with the “tidyverse” collection of packages.\n\nCode will be provid
 ed in a Google Colab Notebook. The expectation is that you follow along ra
 ther than write this code from scratch. \n\n**How to apply:**\n\n[**Apply 
 here**](https://machine-learning-workshop.eventbrite.com.au/)\n\nThis work
 shop is free but participation is subject to application with selection. \
 n\nApplications close at **11:59pm AEST\, Friday 24 May 2024**.\n\nApplica
 tions 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 meeti
 ng link closer to the date. More information on the selection process is p
 rovided in our[ Advice on applying for Australian BioCommons workshops.](h
 ttps://www.biocommons.org.au/workshop-applications)\n\n\n\n_This workshop 
 is presented by the [Australian BioCommons](https://www.biocommons.org.au/
 )\, with the assistance of a network of facilitators from the national [Bi
 oinformatics Training Cooperative](https://www.biocommons.org.au/training-
 cooperative)._\n\n_This event is part of a series of [bioinformatics train
 ing events](https://www.biocommons.org.au/events). If you'd like to hear w
 hen registrations open for other events\, please [subscribe](https://www.b
 iocommons.org.au/subscribe) to Australian BioCommons_
SUMMARY:WORKSHOP: Machine learning in the life sciences
URL;VALUE=URI:https://www.biocommons.org.au/events/machine-learning
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