WORKSHOP: Introduction to Machine Learning in R - from data to knowledge

This record includes training materials associated with the Australian BioCommons workshop ‘Introduction to Machine Learning in R - from data to knowledge’. This workshop took place over one, 4 hour sessions on 09 December 2024.
Event description
With the rise in high-throughput sequencing technologies, the volume of omics data has grown exponentially. A major issue is to mine useful knowledge from these heterogeneous collections of data. The analysis of complex high-volume data is not trivial and classical tools cannot be used to explore their full potential. Machine Learning (ML), a discipline in which computers perform automated learning without being programmed explicitly and assist humans to make sense of large and complex data sets, can thus be very useful in mining large omics datasets to uncover new insights that can advance the field of bioinformatics.
This hands-on workshop will introduce participants to the ML taxonomy and the applications of common ML algorithms to health data. The workshop will cover the foundational concepts and common methods being used to analyse omics data sets by providing a practical context through the use of basic but widely used R libraries. Participants will acquire an understanding of the standard ML processes, as well as the practical skills in applying them on familiar problems and publicly available real-world data sets.
Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event.
Lead trainers: Dr Fotis Psomopoulos, Senior Researcher,  Institute of Applied Biosciences (INAB), Center for Research and Technology Hellas (CERTH)
Facilitators:

Dr Giorgia Mori, Australian BioCommons
Dr Eden Zhang, Sydney Informatics Hub
Dr Erin Graham, Queensland Cyber Infrastructure Foundation (QCIF)

Infrastructure provision: Uwe Winter, Australian BioCommons
Host: Dr. Giorgia Mori, Australian BioCommons
Training materials
Files and materials included in this record:

Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc.

Training materials webpage

Data and documentation